Alexander Suvorov
660322d3a6
Explanation:
ETC1S encoding is a subset of ETC1, which is using only one color endpoint per 4x4 block. The base color is therefore is always encoded as RGB555 and there is no need to encode block flips. ETC2AS encoding is a subset of ETC2A encoding which is using ETC1S encoding for color and default ETC2A encoding for alpha.
ETC1S/ETC2AS Crunch compression and decompression is based on ETC and DXT Crunch compression and decompression algorithms:
- ETC1S/ETC2AS tiling is performed within the area of 8x8 pixels using DXT1/DXT5 tiling scheme
- ETC1S color endpoints are generated using standard ETC1 optimization
- ETC1S color codebook encoding is equivalent to ETC1 codebook encoding
- ETC1S level encoding is equivalent to DXT1 level encoding
- ETC2AS alpha codebook encoding is equivalent to ETC2A alpha codebook encoding
- ETC2AS level encoding is equivalent to DXT5 level encoding
Testing results suggest that ETC1S/ETC2AS encodings can be used to achieve lower bitrates than ETC1/ETC2A on the Kodak test set while providing equivalent image quality (estimated using PSNR).
DXT Testing:
The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch (revision ea9b8d8).
[Compressing Kodak set without mipmaps using DXT1 encoding]
Original: 1582222 bytes / 28.854 sec
Modified: 1468204 bytes / 5.473 sec
Improvement: 7.21% (compression ratio) / 81.03% (compression time)
[Compressing Kodak set with mipmaps using DXT1 encoding]
Original: 2065243 bytes / 36.925 sec
Modified: 1914805 bytes / 7.297 sec
Improvement: 7.28% (compression ratio) / 80.24% (compression time)
ETC Testing:
The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). The ETC1 quantization parameters have been selected in such a way, so that ETC1 compression gives approximately the same average Luma PSNR as the corresponding DXT1 compression (which is equal to 34.044 dB for the Kodak test set compressed without mipmaps using DXT1 encoding and default quality settings).
[Compressing Kodak set without mipmaps using ETC1 encoding]
Total size: 1607858 bytes
Total time: 12.842 sec
Average bitrate: 1.363 bpp
Average Luma PSNR: 34.050 dB
ETCS Testing:
The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). The ETC1S quantization parameters have been selected in such a way, so that ETC1S compression gives approximately the same average Luma PSNR as the corresponding DXT1 compression (which is equal to 34.044 dB for the Kodak test set compressed without mipmaps using DXT1 encoding and default quality settings).
[Compressing Kodak set without mipmaps using ETC1S encoding]
Total size: 1363676 bytes
Total time: 15.586 sec
Average bitrate: 1.156 bpp
Average Luma PSNR: 34.047 dB
1317 lines
54 KiB
C++
1317 lines
54 KiB
C++
// File: crn_dxt_hc.cpp
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// See Copyright Notice and license at the end of inc/crnlib.h
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#include "crn_core.h"
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#include "crn_dxt_hc.h"
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#include "crn_image_utils.h"
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#include "crn_console.h"
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#include "crn_dxt_fast.h"
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#include "crn_etc.h"
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namespace crnlib {
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typedef vec<6, float> vec6F;
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typedef vec<16, float> vec16F;
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static uint8 g_tile_map[8][2][2] = {
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{{ 0, 0 }, { 0, 0 }},
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{{ 0, 0 }, { 1, 1 }},
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{{ 0, 1 }, { 0, 1 }},
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{{ 0, 0 }, { 1, 2 }},
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{{ 1, 2 }, { 0, 0 }},
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{{ 0, 1 }, { 0, 2 }},
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{{ 1, 0 }, { 2, 0 }},
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{{ 0, 1 }, { 2, 3 }},
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};
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dxt_hc::dxt_hc()
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: m_num_blocks(0),
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m_has_color_blocks(false),
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m_has_etc_color_blocks(false),
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m_has_subblocks(false),
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m_num_alpha_blocks(0),
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m_main_thread_id(crn_get_current_thread_id()),
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m_canceled(false),
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m_pTask_pool(NULL),
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m_prev_phase_index(-1),
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m_prev_percentage_complete(-1) {
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}
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dxt_hc::~dxt_hc() {
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}
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void dxt_hc::clear() {
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m_blocks = 0;
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m_num_blocks = 0;
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m_num_alpha_blocks = 0;
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m_has_color_blocks = false;
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m_color_clusters.clear();
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m_alpha_clusters.clear();
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m_canceled = false;
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m_prev_phase_index = -1;
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m_prev_percentage_complete = -1;
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m_block_weights.clear();
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m_block_encodings.clear();
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for (uint c = 0; c < 3; c++)
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m_block_selectors[c].clear();
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m_color_selectors.clear();
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m_alpha_selectors.clear();
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m_color_selectors_used.clear();
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m_alpha_selectors_used.clear();
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m_tile_indices.clear();
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m_endpoint_indices.clear();
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m_selector_indices.clear();
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m_tiles.clear();
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m_num_tiles = 0;
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}
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bool dxt_hc::compress(
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color_quad_u8 (*blocks)[16],
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crnlib::vector<endpoint_indices_details>& endpoint_indices,
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crnlib::vector<selector_indices_details>& selector_indices,
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crnlib::vector<uint32>& color_endpoints,
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crnlib::vector<uint32>& alpha_endpoints,
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crnlib::vector<uint32>& color_selectors,
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crnlib::vector<uint64>& alpha_selectors,
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const params& p
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) {
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clear();
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m_has_etc_color_blocks = p.m_format == cETC1 || p.m_format == cETC2 || p.m_format == cETC2A || p.m_format == cETC1S || p.m_format == cETC2AS;
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m_has_subblocks = p.m_format == cETC1 || p.m_format == cETC2 || p.m_format == cETC2A;
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m_has_color_blocks = p.m_format == cDXT1 || p.m_format == cDXT5 || m_has_etc_color_blocks;
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m_num_alpha_blocks = p.m_format == cDXT5 || p.m_format == cDXT5A || p.m_format == cETC2A || p.m_format == cETC2AS ? 1 : p.m_format == cDXN_XY || p.m_format == cDXN_YX ? 2 : 0;
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if (!m_has_color_blocks && !m_num_alpha_blocks)
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return false;
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m_blocks = blocks;
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m_main_thread_id = crn_get_current_thread_id();
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m_pTask_pool = p.m_pTask_pool;
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m_params = p;
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uint tile_derating[8] = {0, 1, 1, 2, 2, 2, 2, 3};
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for (uint level = 0; level < p.m_num_levels; level++) {
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float adaptive_tile_color_psnr_derating = p.m_adaptive_tile_color_psnr_derating;
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if (level && adaptive_tile_color_psnr_derating > .25f)
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adaptive_tile_color_psnr_derating = math::maximum(.25f, adaptive_tile_color_psnr_derating / powf(3.0f, static_cast<float>(level)));
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for (uint e = 0; e < 8; e++)
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m_color_derating[level][e] = math::lerp(0.0f, adaptive_tile_color_psnr_derating, tile_derating[e] / 3.0f);
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}
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for (uint e = 0; e < 8; e++)
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m_alpha_derating[e] = math::lerp(0.0f, m_params.m_adaptive_tile_alpha_psnr_derating, tile_derating[e] / 3.0f);
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for (uint i = 0; i < 256; i++)
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m_uint8_to_float[i] = i * 1.0f / 255.0f;
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m_num_blocks = m_params.m_num_blocks;
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m_block_weights.resize(m_num_blocks);
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m_block_encodings.resize(m_num_blocks);
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for (uint c = 0; c < 3; c++)
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m_block_selectors[c].resize(m_num_blocks);
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m_tile_indices.resize(m_num_blocks);
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m_endpoint_indices.resize(m_num_blocks);
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m_selector_indices.resize(m_num_blocks);
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m_tiles.resize(m_num_blocks);
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for (uint level = 0; level < p.m_num_levels; level++) {
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float weight = p.m_levels[level].m_weight;
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for (uint b = p.m_levels[level].m_first_block, bEnd = b + p.m_levels[level].m_num_blocks; b < bEnd; b++)
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m_block_weights[b] = weight;
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}
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for (uint i = 0; i <= m_pTask_pool->get_num_threads(); i++)
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m_pTask_pool->queue_object_task(this, m_has_subblocks ? &dxt_hc::determine_tiles_task_etc : &dxt_hc::determine_tiles_task, i);
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m_pTask_pool->join();
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m_num_tiles = 0;
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for (uint t = 0; t < m_tiles.size(); t++) {
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if (m_tiles[t].pixels.size())
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m_num_tiles++;
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}
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if (m_has_color_blocks)
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determine_color_endpoints();
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if (m_num_alpha_blocks)
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determine_alpha_endpoints();
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if (m_has_color_blocks)
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create_color_selector_codebook();
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if (m_num_alpha_blocks)
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create_alpha_selector_codebook();
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color_endpoints.reserve(color_endpoints.size() + m_color_clusters.size());
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crnlib::vector<uint16> color_endpoints_remap(m_color_clusters.size());
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hash_map<uint32, uint> color_endpoints_map;
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for (uint i = 0; i < m_color_clusters.size(); i++) {
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if (m_color_clusters[i].pixels.size()) {
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uint32 endpoint = m_has_etc_color_blocks ? m_color_clusters[i].first_endpoint :
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dxt1_block::pack_endpoints(m_color_clusters[i].first_endpoint, m_color_clusters[i].second_endpoint);
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hash_map<uint32, uint>::insert_result insert_result = color_endpoints_map.insert(endpoint, color_endpoints.size());
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if (insert_result.second) {
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color_endpoints_remap[i] = color_endpoints.size();
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color_endpoints.push_back(endpoint);
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} else {
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color_endpoints_remap[i] = insert_result.first->second;
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}
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}
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}
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alpha_endpoints.reserve(alpha_endpoints.size() + m_alpha_clusters.size());
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crnlib::vector<uint16> alpha_endpoints_remap(m_alpha_clusters.size());
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hash_map<uint32, uint> alpha_endpoints_map;
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for (uint i = 0; i < m_alpha_clusters.size(); i++) {
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if (m_alpha_clusters[i].pixels.size()) {
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uint32 endpoint = dxt5_block::pack_endpoints(m_alpha_clusters[i].first_endpoint, m_alpha_clusters[i].second_endpoint);
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hash_map<uint32, uint>::insert_result insert_result = alpha_endpoints_map.insert(endpoint, alpha_endpoints.size());
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if (insert_result.second) {
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alpha_endpoints_remap[i] = alpha_endpoints.size();
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alpha_endpoints.push_back(endpoint);
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} else {
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alpha_endpoints_remap[i] = insert_result.first->second;
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}
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}
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}
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color_selectors.reserve(color_selectors.size() + m_color_selectors.size());
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crnlib::vector<uint16> color_selectors_remap(m_color_selectors.size());
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hash_map<uint32, uint> color_selectors_map;
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for (uint i = 0; i < m_color_selectors.size(); i++) {
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if (m_color_selectors_used[i]) {
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hash_map<uint32, uint>::insert_result insert_result = color_selectors_map.insert(m_color_selectors[i], color_selectors.size());
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if (insert_result.second) {
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color_selectors_remap[i] = color_selectors.size();
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color_selectors.push_back(m_color_selectors[i]);
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} else {
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color_selectors_remap[i] = insert_result.first->second;
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}
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}
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}
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alpha_selectors.reserve(alpha_selectors.size() + m_alpha_selectors.size());
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crnlib::vector<uint16> alpha_selectors_remap(m_alpha_selectors.size());
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hash_map<uint64, uint> alpha_selectors_map;
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for (uint i = 0; i < m_alpha_selectors.size(); i++) {
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if (m_alpha_selectors_used[i]) {
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hash_map<uint64, uint>::insert_result insert_result = alpha_selectors_map.insert(m_alpha_selectors[i], alpha_selectors.size());
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if (insert_result.second) {
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alpha_selectors_remap[i] = alpha_selectors.size();
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alpha_selectors.push_back(m_alpha_selectors[i]);
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} else {
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alpha_selectors_remap[i] = insert_result.first->second;
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}
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}
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}
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endpoint_indices.resize(m_num_blocks);
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selector_indices.resize(m_num_blocks);
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for (uint level = 0; level < p.m_num_levels; level++) {
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uint first_block = p.m_levels[level].m_first_block;
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uint end_block = first_block + p.m_levels[level].m_num_blocks;
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uint block_width = p.m_levels[level].m_block_width;
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for (uint by = 0, b = first_block; b < end_block; by++) {
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for (uint bx = 0; bx < block_width; bx++, b++) {
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bool top_match = by != 0;
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bool left_match = top_match || bx;
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bool diag_match = m_has_subblocks && top_match && bx;
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for (uint c = m_has_color_blocks ? 0 : cAlpha0; c < cAlpha0 + m_num_alpha_blocks; c++) {
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uint16 endpoint_index = (c ? alpha_endpoints_remap : color_endpoints_remap)[m_endpoint_indices[b].component[c]];
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left_match = left_match && endpoint_index == endpoint_indices[b - 1].component[c];
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top_match = top_match && endpoint_index == endpoint_indices[b - block_width].component[c];
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diag_match = diag_match && endpoint_index == endpoint_indices[b - block_width - 1].component[c];
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endpoint_indices[b].component[c] = endpoint_index;
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uint16 selector_index = (c ? alpha_selectors_remap : color_selectors_remap)[m_selector_indices[b].component[c]];
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selector_indices[b].component[c] = selector_index;
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}
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endpoint_indices[b].reference = m_has_subblocks && b & 1 ? m_endpoint_indices[b].reference : left_match ? 1 : top_match ? 2 : diag_match ? 3 : 0;
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}
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}
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}
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m_pTask_pool = NULL;
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return true;
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}
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vec6F dxt_hc::palettize_color(color_quad_u8* pixels, uint pixels_count) {
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uint color[64];
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for (uint i = 0; i < pixels_count; i++)
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color[i] = pixels[i][0] << 16 | pixels[i][1] << 8 | pixels[i][2];
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std::sort(color, color + pixels_count);
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vec3F vectors[64];
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uint weights[64];
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uint size = 0;
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for (uint i = 0; i < pixels_count; i++) {
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if (!i || color[i] != color[i - 1]) {
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vectors[size][0] = m_params.m_perceptual ? m_uint8_to_float[color[i] >> 16] * 0.5f : m_uint8_to_float[color[i] >> 16];
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vectors[size][1] = m_uint8_to_float[color[i] >> 8 & 0xFF];
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vectors[size][2] = m_params.m_perceptual ? m_uint8_to_float[color[i] & 0xFF] * 0.25f : m_uint8_to_float[color[i] & 0xFF];
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weights[size] = 1;
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size++;
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} else {
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weights[size - 1]++;
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}
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}
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vec3F result[2];
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split_vectors<vec3F>(vectors, weights, size, result);
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if (result[0].length() > result[1].length())
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utils::swap(result[0], result[1]);
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return *(vec6F*)result;
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}
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vec2F dxt_hc::palettize_alpha(color_quad_u8* pixels, uint pixels_count, uint comp_index) {
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uint8 alpha[64];
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for (uint p = 0; p < pixels_count; p++)
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alpha[p] = pixels[p][comp_index];
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std::sort(alpha, alpha + pixels_count);
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vec1F vectors[64];
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uint weights[64];
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uint size = 0;
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for (uint i = 0; i < pixels_count; i++) {
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if (!i || alpha[i] != alpha[i - 1]) {
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vectors[size][0] = m_uint8_to_float[alpha[i]];
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weights[size] = 1;
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size++;
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} else {
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weights[size - 1]++;
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}
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}
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vec1F result[2];
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split_vectors<vec1F>(vectors, weights, size, result);
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if (result[0] > result[1])
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utils::swap(result[0], result[1]);
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return *(vec2F*)result;
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}
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void dxt_hc::determine_tiles_task(uint64 data, void*) {
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uint num_tasks = m_pTask_pool->get_num_threads() + 1;
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uint offsets[9] = {0, 16, 32, 48, 0, 32, 64, 96, 64};
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uint8 tiles[8][4] = {{8}, {6, 7}, {4, 5}, {6, 1, 3}, {7, 0, 2}, {4, 2, 3}, {5, 0, 1}, {0, 2, 1, 3}};
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color_quad_u8 tilePixels[128];
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uint8 selectors[64];
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uint tile_error[3][9];
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uint total_error[3][8];
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etc1_optimizer optimizer;
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etc1_optimizer::params params;
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params.m_use_color4 = false;
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params.m_constrain_against_base_color5 = false;
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etc1_optimizer::results results;
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results.m_pSelectors = selectors;
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int scan[] = {-1, 0, 1};
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int refine[] = {-3, -2, 2, 3};
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for (uint level = 0; level < m_params.m_num_levels; level++) {
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float weight = m_params.m_levels[level].m_weight;
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uint width = m_params.m_levels[level].m_block_width;
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uint height = m_params.m_levels[level].m_num_blocks / width;
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uint faceHeight = height / m_params.m_num_faces;
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uint h = height * data / num_tasks & ~1;
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uint hEnd = height * (data + 1) / num_tasks & ~1;
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uint hFace = h % faceHeight;
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uint b = m_params.m_levels[level].m_first_block + h * width;
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for (; h < hEnd; h += 2, hFace += 2, b += width) {
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uint tile_offset = b;
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uint tile_offset_delta = 4;
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if (hFace == faceHeight) {
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hFace = 0;
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} else if (hFace & 2) {
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tile_offset_delta = -4;
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tile_offset += (width << 1) + tile_offset_delta;
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}
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for (uint bNext = b + width; b < bNext; b += 2, tile_offset += tile_offset_delta) {
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for (int t = 0; t < 64; t += 16)
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memcpy(tilePixels + t, m_blocks[b + (t & 16 ? width : 0) + (t & 32 ? 1 : 0)], 64);
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for (int t = 0; t < 64; t += 4)
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memcpy(tilePixels + 64 + t, m_blocks[b + (t & 32 ? width : 0) + (t & 4 ? 1 : 0)] + (t >> 1 & 12), 16);
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for (uint t = 0; t < 9; t++) {
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color_quad_u8* pixels = tilePixels + offsets[t];
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uint size = 16 << (t >> 2);
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if (m_has_etc_color_blocks) {
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params.m_pSrc_pixels = pixels;
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params.m_num_src_pixels = results.m_n = size;
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optimizer.init(params, results);
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params.m_pScan_deltas = scan;
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params.m_scan_delta_size = sizeof(scan) / sizeof(*scan);
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optimizer.compute();
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if (results.m_error > 375 * params.m_num_src_pixels) {
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params.m_pScan_deltas = refine;
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params.m_scan_delta_size = sizeof(refine) / sizeof(*refine);
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optimizer.compute();
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}
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tile_error[cColor][t] = results.m_error;
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} else if (m_has_color_blocks) {
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uint low16, high16;
|
|
dxt_fast::compress_color_block(size, pixels, low16, high16, selectors);
|
|
color_quad_u8 block_colors[4];
|
|
dxt1_block::get_block_colors4(block_colors, low16, high16);
|
|
uint error = 0;
|
|
for (uint p = 0; p < size; p++) {
|
|
for (uint8 c = 0; c < 3; c++) {
|
|
uint delta = pixels[p][c] - block_colors[selectors[p]][c];
|
|
error += delta * delta;
|
|
}
|
|
}
|
|
tile_error[cColor][t] = error;
|
|
}
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
uint8 component = m_params.m_alpha_component_indices[a];
|
|
dxt5_endpoint_optimizer optimizer;
|
|
dxt5_endpoint_optimizer::params params;
|
|
dxt5_endpoint_optimizer::results results;
|
|
params.m_pPixels = pixels;
|
|
params.m_num_pixels = size;
|
|
params.m_comp_index = component;
|
|
params.m_use_both_block_types = false;
|
|
params.m_quality = cCRNDXTQualityNormal;
|
|
results.m_pSelectors = selectors;
|
|
optimizer.compute(params, results);
|
|
uint block_values[cDXT5SelectorValues];
|
|
dxt5_block::get_block_values8(block_values, results.m_first_endpoint, results.m_second_endpoint);
|
|
tile_error[cAlpha0 + a][t] = results.m_error;
|
|
}
|
|
}
|
|
|
|
for (uint8 c = m_has_color_blocks ? 0 : cAlpha0; c < cAlpha0 + m_num_alpha_blocks; c++) {
|
|
for (uint8 e = 0; e < 8; e++) {
|
|
total_error[c][e] = 0;
|
|
for (uint8 t = 0, s = e + 1; s; s >>= 1, t++)
|
|
total_error[c][e] += tile_error[c][tiles[e][t]];
|
|
}
|
|
}
|
|
|
|
float best_quality = 0.0f;
|
|
uint best_encoding = 0;
|
|
for (uint e = 0; e < 8; e++) {
|
|
float quality = 0;
|
|
if (m_has_color_blocks) {
|
|
double peakSNR = total_error[cColor][e] ? log10(255.0f / sqrt(total_error[cColor][e] / 192.0)) * 20.0f : 999999.0f;
|
|
quality = (float)math::maximum<double>(peakSNR - m_color_derating[level][e], 0.0f);
|
|
if (m_num_alpha_blocks)
|
|
quality *= m_params.m_adaptive_tile_color_alpha_weighting_ratio;
|
|
}
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
double peakSNR = total_error[cAlpha0 + a][e] ? log10(255.0f / sqrt(total_error[cAlpha0 + a][e] / 64.0)) * 20.0f : 999999.0f;
|
|
quality += (float)math::maximum<double>(peakSNR - m_alpha_derating[e], 0.0f);
|
|
}
|
|
if (quality > best_quality) {
|
|
best_quality = quality;
|
|
best_encoding = e;
|
|
}
|
|
}
|
|
|
|
for (uint tile_index = 0, s = best_encoding + 1; s; s >>= 1, tile_index++) {
|
|
tile_details& tile = m_tiles[tile_offset | tile_index];
|
|
uint t = tiles[best_encoding][tile_index];
|
|
tile.pixels.append(tilePixels + offsets[t], 16 << (t >> 2));
|
|
tile.weight = weight;
|
|
if (m_has_color_blocks)
|
|
tile.color_endpoint = palettize_color(tile.pixels.get_ptr(), tile.pixels.size());
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++)
|
|
tile.alpha_endpoints[a] = palettize_alpha(tile.pixels.get_ptr(), tile.pixels.size(), m_params.m_alpha_component_indices[a]);
|
|
}
|
|
|
|
for (uint by = 0; by < 2; by++) {
|
|
for (uint bx = 0; bx < 2; bx++) {
|
|
m_block_encodings[b + (by ? width : 0) + bx] = best_encoding;
|
|
m_tile_indices[b + (by ? width : 0) + bx] = tile_offset | g_tile_map[best_encoding][by][bx];
|
|
}
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_tiles_task_etc(uint64 data, void*) {
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
uint offsets[5] = {0, 8, 16, 24, 16};
|
|
uint8 tiles[3][2] = {{4}, {2, 3}, {0, 1}};
|
|
uint8 tile_map[3][2] = {{ 0, 0 }, { 0, 1 }, { 0, 1 }};
|
|
color_quad_u8 tilePixels[32];
|
|
uint8 selectors[32];
|
|
uint tile_error[5];
|
|
uint total_error[3];
|
|
|
|
etc1_optimizer optimizer;
|
|
etc1_optimizer::params params;
|
|
params.m_use_color4 = false;
|
|
params.m_constrain_against_base_color5 = false;
|
|
etc1_optimizer::results results;
|
|
results.m_pSelectors = selectors;
|
|
int scan[] = {-1, 0, 1};
|
|
int refine[] = {-3, -2, 2, 3};
|
|
|
|
for (uint level = 0; level < m_params.m_num_levels; level++) {
|
|
float weight = m_params.m_levels[level].m_weight;
|
|
uint b = (m_params.m_levels[level].m_first_block + m_params.m_levels[level].m_num_blocks * data / num_tasks) & ~1;
|
|
uint bEnd = (m_params.m_levels[level].m_first_block + m_params.m_levels[level].m_num_blocks * (data + 1) / num_tasks) & ~1;
|
|
for (; b < bEnd; b += 2) {
|
|
for (uint p = 0; p < 16; p++)
|
|
tilePixels[p] = m_blocks[b >> 1][(p << 2 & 12) | p >> 2];
|
|
memcpy(tilePixels + 16, m_blocks[b >> 1], 64);
|
|
for (uint t = 0; t < 5; t++) {
|
|
params.m_pSrc_pixels = tilePixels + offsets[t];
|
|
params.m_num_src_pixels = results.m_n = 8 << (t >> 2);
|
|
optimizer.init(params, results);
|
|
params.m_pScan_deltas = scan;
|
|
params.m_scan_delta_size = sizeof(scan) / sizeof(*scan);
|
|
optimizer.compute();
|
|
if (results.m_error > 375 * params.m_num_src_pixels) {
|
|
params.m_pScan_deltas = refine;
|
|
params.m_scan_delta_size = sizeof(refine) / sizeof(*refine);
|
|
optimizer.compute();
|
|
}
|
|
tile_error[t] = results.m_error;
|
|
}
|
|
|
|
for (uint8 e = 0; e < 3; e++) {
|
|
total_error[e] = 0;
|
|
for (uint8 t = 0, s = e + 1; s; s >>= 1, t++)
|
|
total_error[e] += tile_error[tiles[e][t]];
|
|
}
|
|
|
|
float best_quality = 0.0f;
|
|
uint best_encoding = 0;
|
|
for (uint e = 0; e < 3; e++) {
|
|
float quality = 0;
|
|
double peakSNR = total_error[e] ? log10(255.0f / sqrt(total_error[e] / 48.0)) * 20.0f : 999999.0f;
|
|
quality = (float)math::maximum<double>(peakSNR - m_color_derating[level][e], 0.0f);
|
|
if (quality > best_quality) {
|
|
best_quality = quality;
|
|
best_encoding = e;
|
|
}
|
|
}
|
|
|
|
vec2F alpha_endpoints = m_num_alpha_blocks ? palettize_alpha(tilePixels, 16, 3) : vec2F(cClear);
|
|
for (uint tile_index = 0, s = best_encoding + 1; s; s >>= 1, tile_index++) {
|
|
tile_details& tile = m_tiles[b | tile_index];
|
|
uint t = tiles[best_encoding][tile_index];
|
|
tile.pixels.append(tilePixels + offsets[t], 8 << (t >> 2));
|
|
tile.weight = weight;
|
|
tile.color_endpoint = palettize_color(tile.pixels.get_ptr(), tile.pixels.size());
|
|
if (m_num_alpha_blocks)
|
|
tile.alpha_endpoints[0] = alpha_endpoints;
|
|
}
|
|
|
|
for (uint bx = 0; bx < 2; bx++) {
|
|
m_block_encodings[b | bx] = best_encoding;
|
|
m_tile_indices[b | bx] = b | tile_map[best_encoding][bx];
|
|
m_endpoint_indices[b | bx].reference = bx ? best_encoding : 0;
|
|
}
|
|
if (best_encoding >> 1)
|
|
memcpy(m_blocks[b >> 1], tilePixels, 64);
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoint_codebook_task(uint64 data, void*) {
|
|
const uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
dxt1_endpoint_optimizer optimizer;
|
|
dxt_endpoint_refiner refiner;
|
|
crnlib::vector<uint8> selectors;
|
|
|
|
for (uint cluster_index = (uint)data; cluster_index < m_color_clusters.size(); cluster_index += num_tasks) {
|
|
color_cluster& cluster = m_color_clusters[cluster_index];
|
|
if (cluster.pixels.empty())
|
|
continue;
|
|
|
|
dxt1_endpoint_optimizer::params params;
|
|
params.m_block_index = cluster_index;
|
|
params.m_pPixels = cluster.pixels.get_ptr();
|
|
params.m_num_pixels = cluster.pixels.size();
|
|
params.m_pixels_have_alpha = false;
|
|
params.m_use_alpha_blocks = false;
|
|
params.m_perceptual = m_params.m_perceptual;
|
|
params.m_quality = cCRNDXTQualityUber;
|
|
params.m_endpoint_caching = false;
|
|
|
|
dxt1_endpoint_optimizer::results results;
|
|
selectors.resize(params.m_num_pixels);
|
|
results.m_pSelectors = selectors.get_ptr();
|
|
|
|
optimizer.compute(params, results);
|
|
cluster.first_endpoint = results.m_low_color;
|
|
cluster.second_endpoint = results.m_high_color;
|
|
color_quad_u8 block_values[4], color_values[4];
|
|
dxt1_block::get_block_colors4(block_values, cluster.first_endpoint, cluster.second_endpoint);
|
|
for (uint i = 0; i < 4; i++)
|
|
color_values[i] = cluster.color_values[i] = block_values[g_dxt1_from_linear[i]];
|
|
for (uint c = 0; results.m_alternate_rounding && c < 3; c++) {
|
|
color_values[1].c[c] = ((color_values[0].c[c] << 1) + color_values[3].c[c] + 1) / 3;
|
|
color_values[2].c[c] = ((color_values[3].c[c] << 1) + color_values[0].c[c] + 1) / 3;
|
|
}
|
|
|
|
uint endpoint_weight = color::color_distance(m_params.m_perceptual, color_values[0], color_values[3], false) / 2000;
|
|
float encoding_weight[8];
|
|
for (uint i = 0; i < 8; i++)
|
|
encoding_weight[i] = math::lerp(1.15f, 1.0f, i / 7.0f);
|
|
|
|
crnlib::vector<uint>& blocks = cluster.blocks[cColor];
|
|
for (uint i = 0; i < blocks.size(); i++) {
|
|
uint b = blocks[i];
|
|
uint weight = (uint)(math::clamp<uint>(endpoint_weight * m_block_weights[b], 1, 2048) * encoding_weight[m_block_encodings[b]]);
|
|
uint32 selector = 0;
|
|
for (uint p = 0; p < 16; p++) {
|
|
uint error_best = cUINT32_MAX;
|
|
uint8 s_best = 0;
|
|
for (uint8 t = 0; t < 4; t++) {
|
|
uint8 s = results.m_reordered ? 3 - g_dxt1_to_linear[t] : g_dxt1_to_linear[t];
|
|
uint error = color::color_distance(m_params.m_perceptual, (color_quad_u8&)m_blocks[b][p], color_values[s], false);
|
|
if (error < error_best) {
|
|
s_best = s;
|
|
error_best = error;
|
|
}
|
|
}
|
|
selector = selector << 2 | s_best;
|
|
}
|
|
m_block_selectors[cColor][b] = (uint64)selector << 32 | weight;
|
|
}
|
|
|
|
dxt_endpoint_refiner::params refinerParams;
|
|
dxt_endpoint_refiner::results refinerResults;
|
|
refinerParams.m_perceptual = m_params.m_perceptual;
|
|
refinerParams.m_pSelectors = selectors.get_ptr();
|
|
refinerParams.m_pPixels = cluster.pixels.get_ptr();
|
|
refinerParams.m_num_pixels = cluster.pixels.size();
|
|
refinerParams.m_dxt1_selectors = true;
|
|
refinerParams.m_error_to_beat = results.m_error;
|
|
refinerParams.m_block_index = cluster_index;
|
|
if (refiner.refine(refinerParams, refinerResults)) {
|
|
cluster.first_endpoint = refinerResults.m_low_color;
|
|
cluster.second_endpoint = refinerResults.m_high_color;
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoint_codebook_task_etc(uint64 data, void*) {
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
uint8 delta[8][2] = { {2, 8}, {5, 17}, {9, 29}, {13, 42}, {18, 60}, {24, 80}, {33, 106}, {47, 183} };
|
|
int scan[] = {-1, 0, 1};
|
|
int refine[] = {-3, -2, 2, 3};
|
|
for (uint iCluster = m_color_clusters.size() * data / num_tasks, iEnd = m_color_clusters.size() * (data + 1) / num_tasks; iCluster < iEnd; iCluster++) {
|
|
color_cluster& cluster = m_color_clusters[iCluster];
|
|
if (cluster.pixels.size()) {
|
|
etc1_optimizer optimizer;
|
|
etc1_optimizer::params params;
|
|
params.m_use_color4 = false;
|
|
params.m_constrain_against_base_color5 = false;
|
|
etc1_optimizer::results results;
|
|
crnlib::vector<uint8> selectors(cluster.pixels.size());
|
|
params.m_pSrc_pixels = cluster.pixels.get_ptr();
|
|
results.m_pSelectors = selectors.get_ptr();
|
|
results.m_n = params.m_num_src_pixels = cluster.pixels.size();
|
|
optimizer.init(params, results);
|
|
params.m_pScan_deltas = scan;
|
|
params.m_scan_delta_size = sizeof(scan) / sizeof(*scan);
|
|
optimizer.compute();
|
|
if (results.m_error > 375 * params.m_num_src_pixels) {
|
|
params.m_pScan_deltas = refine;
|
|
params.m_scan_delta_size = sizeof(refine) / sizeof(*refine);
|
|
optimizer.compute();
|
|
}
|
|
color_quad_u8 endpoint;
|
|
for (int c = 0; c < 3; c++)
|
|
endpoint.c[c] = results.m_block_color_unscaled.c[c] << 3 | results.m_block_color_unscaled.c[c] >> 2;
|
|
endpoint.c[3] = results.m_block_inten_table;
|
|
cluster.first_endpoint = endpoint.m_u32;
|
|
for (uint8 d0 = delta[endpoint.c[3]][0], d1 = delta[endpoint.c[3]][1], c = 0; c < 3; c++) {
|
|
uint8 q = endpoint.c[c];
|
|
cluster.color_values[0].c[c] = q <= d1 ? 0 : q - d1;
|
|
cluster.color_values[1].c[c] = q <= d0 ? 0 : q - d0;
|
|
cluster.color_values[2].c[c] = q >= 255 - d0 ? 255 : q + d0;
|
|
cluster.color_values[3].c[c] = q >= 255 - d1 ? 255 : q + d1;
|
|
}
|
|
for (int t = 0; t < 4; t++)
|
|
cluster.color_values[t].c[3] = 0xFF;
|
|
float endpoint_weight = powf(math::minimum((cluster.color_values[3].get_luma() - cluster.color_values[0].get_luma()) / 100.0f, 1.0f), 2.7f);
|
|
|
|
crnlib::vector<uint>& blocks = cluster.blocks[cColor];
|
|
uint blockSize = m_has_subblocks ? 8 : 16;
|
|
for (uint i = 0; i < blocks.size(); i++) {
|
|
uint b = blocks[i];
|
|
color_quad_u8* pixels = m_has_subblocks ? ((color_quad_u8(*)[8])m_blocks)[b] : m_blocks[b];
|
|
uint weight = (uint)(math::clamp<uint>(0x8000 * endpoint_weight * m_block_weights[b] * (m_block_encodings[b] ? 0.972f : 1.0f), 1, 0xFFFF));
|
|
uint32 selector = 0;
|
|
for (uint p = 0; p < blockSize; p++) {
|
|
uint error_best = cUINT32_MAX;
|
|
uint8 s_best = 0;
|
|
for (uint8 s = 0; s < 4; s++) {
|
|
uint error = color::color_distance(m_params.m_perceptual, pixels[p], cluster.color_values[s], false);
|
|
if (error < error_best) {
|
|
s_best = s;
|
|
error_best = error;
|
|
}
|
|
}
|
|
selector = selector << 2 | s_best;
|
|
}
|
|
m_block_selectors[cColor][b] = (uint64)selector << (!m_has_subblocks || (b & 1) ? 32 : 48) | weight;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoint_clusters_task(uint64 data, void* pData_ptr) {
|
|
tree_clusterizer<vec6F>* vq = (tree_clusterizer<vec6F>*)pData_ptr;
|
|
const crnlib::vector<vec6F>& codebook = vq->get_codebook();
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
for (uint t = m_tiles.size() * data / num_tasks, tEnd = m_tiles.size() * (data + 1) / num_tasks; t < tEnd; t++) {
|
|
if (m_tiles[t].pixels.size()) {
|
|
const vec6F& v = m_tiles[t].color_endpoint;
|
|
float node_dist = codebook[vq->get_node_index(v)].squared_distance(v);
|
|
float best_dist = math::cNearlyInfinite;
|
|
uint best_index = 0;
|
|
for (uint i = 0; i < codebook.size(); i++) {
|
|
const vec6F& c = codebook[i];
|
|
float dist = 0;
|
|
float d0 = c[0] - v[0]; dist += d0 * d0;
|
|
float d1 = c[1] - v[1]; dist += d1 * d1;
|
|
if (dist > node_dist)
|
|
continue;
|
|
float d2 = c[2] - v[2]; dist += d2 * d2;
|
|
float d3 = c[3] - v[3]; dist += d3 * d3;
|
|
if (dist > node_dist)
|
|
continue;
|
|
float d4 = c[4] - v[4]; dist += d4 * d4;
|
|
float d5 = c[5] - v[5]; dist += d5 * d5;
|
|
if (dist < best_dist) {
|
|
best_dist = dist;
|
|
best_index = i;
|
|
if (best_dist == 0.0f)
|
|
break;
|
|
}
|
|
}
|
|
m_tiles[t].cluster_indices[cColor] = best_index;
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoints() {
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
crnlib::vector<std::pair<vec6F, uint> > endpoints;
|
|
for (uint t = 0; t < m_tiles.size(); t++) {
|
|
if (m_tiles[t].pixels.size())
|
|
endpoints.push_back(std::make_pair(m_tiles[t].color_endpoint, (uint)(m_tiles[t].pixels.size() * m_tiles[t].weight)));
|
|
}
|
|
|
|
struct Node {
|
|
std::pair<vec6F, uint> *p, *pEnd;
|
|
Node (std::pair<vec6F, uint>* begin, std::pair<vec6F, uint>* end) : p(begin), pEnd(end) {}
|
|
bool operator<(const Node& other) const { return *p > *other.p; }
|
|
static void sort_task(uint64 data, void* ptr) { std::sort(((Node*)ptr)->p, ((Node*)ptr)->pEnd); }
|
|
};
|
|
|
|
crnlib::vector<Node> nodes;
|
|
Node node(0, endpoints.get_ptr());
|
|
for (uint i = 0; i < num_tasks; i++) {
|
|
node.p = node.pEnd;
|
|
node.pEnd = endpoints.get_ptr() + endpoints.size() * (i + 1) / num_tasks;
|
|
if (node.p != node.pEnd)
|
|
nodes.push_back(node);
|
|
}
|
|
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
m_pTask_pool->queue_task(&Node::sort_task, i, &nodes[i]);
|
|
m_pTask_pool->join();
|
|
|
|
std::priority_queue<Node> queue;
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
queue.push(nodes[i]);
|
|
|
|
crnlib::vector<vec6F> vectors;
|
|
crnlib::vector<uint> weights;
|
|
vectors.reserve(endpoints.size());
|
|
weights.reserve(endpoints.size());
|
|
while (queue.size()) {
|
|
Node node = queue.top();
|
|
std::pair<vec6F, uint>* endpoint = node.p++;
|
|
queue.pop();
|
|
if (node.p != node.pEnd)
|
|
queue.push(node);
|
|
if (!vectors.size() || endpoint->first != vectors.back()) {
|
|
vectors.push_back(endpoint->first);
|
|
weights.push_back(endpoint->second);
|
|
} else if (weights.back() > UINT_MAX - endpoint->second) {
|
|
weights.back() = UINT_MAX;
|
|
} else {
|
|
weights.back() += endpoint->second;
|
|
}
|
|
}
|
|
|
|
tree_clusterizer<vec6F> vq;
|
|
vq.generate_codebook(vectors.get_ptr(), weights.get_ptr(), vectors.size(), math::minimum<uint>(m_num_tiles, m_params.m_color_endpoint_codebook_size), true, m_pTask_pool);
|
|
m_color_clusters.resize(vq.get_codebook_size());
|
|
|
|
for (uint i = 0; i <= m_pTask_pool->get_num_threads(); i++)
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::determine_color_endpoint_clusters_task, i, &vq);
|
|
m_pTask_pool->join();
|
|
|
|
for (uint t = 0; t < m_num_blocks; t++) {
|
|
if (m_tiles[t].pixels.size())
|
|
m_color_clusters[m_tiles[t].cluster_indices[cColor]].pixels.append(m_tiles[t].pixels);
|
|
}
|
|
|
|
for (uint b = 0; b < m_num_blocks; b++) {
|
|
uint cluster_index = m_tiles[m_tile_indices[b]].cluster_indices[cColor];
|
|
m_endpoint_indices[b].component[cColor] = cluster_index;
|
|
m_color_clusters[cluster_index].blocks[cColor].push_back(b);
|
|
if (m_has_subblocks && m_endpoint_indices[b].reference && cluster_index == m_endpoint_indices[b - 1].component[cColor]) {
|
|
if (m_endpoint_indices[b].reference >> 1) {
|
|
color_quad_u8 mirror[16];
|
|
for (uint p = 0; p < 16; p++)
|
|
mirror[p] = m_blocks[b >> 1][(p << 2 & 12) | p >> 2];
|
|
memcpy(m_blocks[b >> 1], mirror, 64);
|
|
}
|
|
m_endpoint_indices[b].reference = 0;
|
|
}
|
|
}
|
|
|
|
for (uint i = 0; i <= m_pTask_pool->get_num_threads(); i++)
|
|
m_pTask_pool->queue_object_task(this, m_has_etc_color_blocks ? &dxt_hc::determine_color_endpoint_codebook_task_etc : &dxt_hc::determine_color_endpoint_codebook_task, i, NULL);
|
|
m_pTask_pool->join();
|
|
}
|
|
|
|
void dxt_hc::determine_alpha_endpoint_codebook_task(uint64 data, void*) {
|
|
const uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
dxt5_endpoint_optimizer optimizer;
|
|
dxt_endpoint_refiner refiner;
|
|
crnlib::vector<uint8> selectors;
|
|
|
|
for (uint cluster_index = (uint)data; cluster_index < m_alpha_clusters.size(); cluster_index += num_tasks) {
|
|
alpha_cluster& cluster = m_alpha_clusters[cluster_index];
|
|
if (cluster.pixels.empty())
|
|
continue;
|
|
|
|
dxt5_endpoint_optimizer::params params;
|
|
params.m_pPixels = cluster.pixels.get_ptr();
|
|
params.m_num_pixels = cluster.pixels.size();
|
|
params.m_comp_index = 0;
|
|
params.m_quality = cCRNDXTQualityUber;
|
|
params.m_use_both_block_types = false;
|
|
|
|
dxt5_endpoint_optimizer::results results;
|
|
selectors.resize(params.m_num_pixels);
|
|
results.m_pSelectors = selectors.get_ptr();
|
|
|
|
optimizer.compute(params, results);
|
|
cluster.first_endpoint = results.m_first_endpoint;
|
|
cluster.second_endpoint = results.m_second_endpoint;
|
|
uint block_values[8], alpha_values[8];
|
|
dxt5_block::get_block_values(block_values, cluster.first_endpoint, cluster.second_endpoint);
|
|
for (uint i = 0; i < 8; i++)
|
|
alpha_values[i] = cluster.alpha_values[i] = block_values[g_dxt5_from_linear[i]];
|
|
int delta = cluster.first_endpoint - cluster.second_endpoint;
|
|
uint encoding_weight[8];
|
|
for (uint endpoint_weight = math::clamp<uint>(delta * delta >> 3, 1, 2048), i = 0; i < 8; i++)
|
|
encoding_weight[i] = (uint)(endpoint_weight * math::lerp(1.15f, 1.0f, i / 7.0f));
|
|
|
|
if (m_has_etc_color_blocks) {
|
|
static const int stripped_modifier_table[2][8] = {
|
|
{-10, -7, -5, -2, 1, 4, 6, 9},
|
|
{-10, -3, -2, -1, 0, 1, 2, 9}
|
|
};
|
|
int base_codeword = (results.m_first_endpoint + results.m_second_endpoint + 1) >> 1;
|
|
int modifier_index = delta <= 6 ? 13 : 11;
|
|
int multiplier = delta <= 6 ? 1 : math::clamp<int>((delta + 12) / 18, 1, 15);
|
|
const int* modifier = stripped_modifier_table[modifier_index == 11 ? 0 : 1];
|
|
for (int i = 0; i < 8; i++)
|
|
alpha_values[i] = cluster.alpha_values[i] = math::clamp<int>(base_codeword + modifier[i] * multiplier, 0, 255);
|
|
cluster.first_endpoint = base_codeword;
|
|
cluster.second_endpoint = multiplier << 4 | modifier_index;
|
|
}
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
uint component_index = m_params.m_alpha_component_indices[a];
|
|
crnlib::vector<uint>& blocks = cluster.blocks[cAlpha0 + a];
|
|
for (uint i = 0; i < blocks.size(); i++) {
|
|
uint b = blocks[i];
|
|
uint weight = encoding_weight[m_block_encodings[b]];
|
|
uint64 selector = 0;
|
|
for (uint p = 0; p < 16; p++) {
|
|
uint error_best = cUINT32_MAX;
|
|
uint8 s_best = 0;
|
|
for (uint8 t = 0; t < 8; t++) {
|
|
uint8 s = m_has_etc_color_blocks ? t : results.m_reordered ? 7 - g_dxt5_to_linear[t] : g_dxt5_to_linear[t];
|
|
int delta = m_blocks[m_has_subblocks ? b >> 1 : b][p][component_index] - alpha_values[s];
|
|
uint error = delta >= 0 ? delta : -delta;
|
|
if (error < error_best) {
|
|
s_best = s;
|
|
error_best = error;
|
|
}
|
|
}
|
|
selector = selector << 3 | s_best;
|
|
}
|
|
m_block_selectors[cAlpha0 + a][b] = selector << 16 | weight;
|
|
}
|
|
}
|
|
|
|
dxt_endpoint_refiner::params refinerParams;
|
|
dxt_endpoint_refiner::results refinerResults;
|
|
refinerParams.m_perceptual = m_params.m_perceptual;
|
|
refinerParams.m_pSelectors = selectors.get_ptr();
|
|
refinerParams.m_pPixels = cluster.pixels.get_ptr();
|
|
refinerParams.m_num_pixels = cluster.pixels.size();
|
|
refinerParams.m_dxt1_selectors = false;
|
|
refinerParams.m_error_to_beat = results.m_error;
|
|
refinerParams.m_block_index = cluster_index;
|
|
cluster.refined_alpha = !m_has_etc_color_blocks && refiner.refine(refinerParams, refinerResults);
|
|
if (cluster.refined_alpha) {
|
|
cluster.first_endpoint = refinerResults.m_low_color;
|
|
cluster.second_endpoint = refinerResults.m_high_color;
|
|
dxt5_block::get_block_values(block_values, cluster.first_endpoint, cluster.second_endpoint);
|
|
for (uint i = 0; i < 8; i++)
|
|
cluster.refined_alpha_values[i] = block_values[g_dxt5_from_linear[i]];
|
|
} else {
|
|
memcpy(cluster.refined_alpha_values, cluster.alpha_values, sizeof(cluster.refined_alpha_values));
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_alpha_endpoint_clusters_task(uint64 data, void* pData_ptr) {
|
|
tree_clusterizer<vec2F>* vq = (tree_clusterizer<vec2F>*)pData_ptr;
|
|
const crnlib::vector<vec2F>& codebook = vq->get_codebook();
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
for (uint t = m_tiles.size() * data / num_tasks, tEnd = m_tiles.size() * (data + 1) / num_tasks; t < tEnd; t++) {
|
|
if (m_tiles[t].pixels.size()) {
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
const vec2F& v = m_tiles[t].alpha_endpoints[a];
|
|
float best_dist = math::cNearlyInfinite;
|
|
uint best_index = 0;
|
|
for (uint i = 0; i < codebook.size(); i++) {
|
|
float dist = (codebook[i][0] - v[0]) * (codebook[i][0] - v[0]) + (codebook[i][1] - v[1]) * (codebook[i][1] - v[1]);
|
|
if (dist < best_dist) {
|
|
best_dist = dist;
|
|
best_index = i;
|
|
if (best_dist == 0.0f)
|
|
break;
|
|
}
|
|
}
|
|
m_tiles[t].cluster_indices[cAlpha0 + a] = best_index;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_alpha_endpoints() {
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
crnlib::vector<std::pair<vec2F, uint> > endpoints;
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
for (uint t = 0; t < m_tiles.size(); t++) {
|
|
if (m_tiles[t].pixels.size())
|
|
endpoints.push_back(std::make_pair(m_tiles[t].alpha_endpoints[a], m_tiles[t].pixels.size()));
|
|
}
|
|
}
|
|
|
|
struct Node {
|
|
std::pair<vec2F, uint> *p, *pEnd;
|
|
Node (std::pair<vec2F, uint>* begin, std::pair<vec2F, uint>* end) : p(begin), pEnd(end) {}
|
|
bool operator<(const Node& other) const { return *p > *other.p; }
|
|
static void sort_task(uint64 data, void* ptr) { std::sort(((Node*)ptr)->p, ((Node*)ptr)->pEnd); }
|
|
};
|
|
|
|
crnlib::vector<Node> nodes;
|
|
Node node(0, endpoints.get_ptr());
|
|
for (uint i = 0; i < num_tasks; i++) {
|
|
node.p = node.pEnd;
|
|
node.pEnd = endpoints.get_ptr() + endpoints.size() * (i + 1) / num_tasks;
|
|
if (node.p != node.pEnd)
|
|
nodes.push_back(node);
|
|
}
|
|
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
m_pTask_pool->queue_task(&Node::sort_task, i, &nodes[i]);
|
|
m_pTask_pool->join();
|
|
|
|
std::priority_queue<Node> queue;
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
queue.push(nodes[i]);
|
|
|
|
crnlib::vector<vec2F> vectors;
|
|
crnlib::vector<uint> weights;
|
|
vectors.reserve(endpoints.size());
|
|
weights.reserve(endpoints.size());
|
|
while (queue.size()) {
|
|
Node node = queue.top();
|
|
std::pair<vec2F, uint>* endpoint = node.p++;
|
|
queue.pop();
|
|
if (node.p != node.pEnd)
|
|
queue.push(node);
|
|
if (!vectors.size() || endpoint->first != vectors.back()) {
|
|
vectors.push_back(endpoint->first);
|
|
weights.push_back(endpoint->second);
|
|
} else if (weights.back() > UINT_MAX - endpoint->second) {
|
|
weights.back() = UINT_MAX;
|
|
} else {
|
|
weights.back() += endpoint->second;
|
|
}
|
|
}
|
|
|
|
tree_clusterizer<vec2F> vq;
|
|
vq.generate_codebook(vectors.get_ptr(), weights.get_ptr(), vectors.size(), math::minimum<uint>(m_num_tiles, m_params.m_alpha_endpoint_codebook_size), false, m_pTask_pool);
|
|
m_alpha_clusters.resize(vq.get_codebook_size());
|
|
|
|
for (uint i = 0; i < num_tasks; i++)
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::determine_alpha_endpoint_clusters_task, i, &vq);
|
|
m_pTask_pool->join();
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
uint component_index = m_params.m_alpha_component_indices[a];
|
|
for (uint t = 0; t < m_num_blocks; t++) {
|
|
crnlib::vector<color_quad_u8>& source = m_tiles[t].pixels;
|
|
if (source.size()) {
|
|
crnlib::vector<color_quad_u8>& destination = m_alpha_clusters[m_tiles[t].cluster_indices[cAlpha0 + a]].pixels;
|
|
for (uint p = 0; p < source.size(); p++)
|
|
destination.push_back(color_quad_u8(source[p][component_index]));
|
|
}
|
|
}
|
|
}
|
|
|
|
for (uint b = 0; b < m_num_blocks; b++) {
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
uint cluster_index = m_tiles[m_tile_indices[b]].cluster_indices[cAlpha0 + a];
|
|
m_endpoint_indices[b].component[cAlpha0 + a] = cluster_index;
|
|
if (!(m_has_subblocks && b & 1))
|
|
m_alpha_clusters[cluster_index].blocks[cAlpha0 + a].push_back(b);
|
|
}
|
|
}
|
|
|
|
for (uint i = 0; i < num_tasks; i++)
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::determine_alpha_endpoint_codebook_task, i, NULL);
|
|
m_pTask_pool->join();
|
|
}
|
|
|
|
struct color_selector_details {
|
|
color_selector_details() { utils::zero_object(*this); }
|
|
uint error[16][4];
|
|
bool used;
|
|
};
|
|
|
|
void dxt_hc::create_color_selector_codebook_task(uint64 data, void* pData_ptr) {
|
|
crnlib::vector<color_selector_details>& selector_details = *static_cast<crnlib::vector<color_selector_details>*>(pData_ptr);
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
uint E2[16][4];
|
|
uint E4[8][16];
|
|
uint E8[4][256];
|
|
for (uint n = m_has_subblocks ? m_num_blocks >> 1 : m_num_blocks, b = n * data / num_tasks, bEnd = n * (data + 1) / num_tasks; b < bEnd; b++) {
|
|
color_cluster& cluster = m_color_clusters[m_endpoint_indices[b].color];
|
|
color_quad_u8* endpoint_colors = cluster.color_values;
|
|
for (uint p = 0; p < 16; p++) {
|
|
for (uint s = 0; s < 4; s++)
|
|
E2[p][s] = m_has_subblocks ? color::color_distance(m_params.m_perceptual, m_blocks[b][p], m_color_clusters[m_endpoint_indices[b << 1 | p >> 3].color].color_values[s], false) :
|
|
color::color_distance(m_params.m_perceptual, m_blocks[b][p], endpoint_colors[s], false);
|
|
}
|
|
for (uint p = 0; p < 8; p++) {
|
|
for (uint s = 0; s < 16; s++)
|
|
E4[p][s] = E2[p << 1][s & 3] + E2[p << 1 | 1][s >> 2];
|
|
}
|
|
for (uint p = 0; p < 4; p++) {
|
|
for (uint s = 0; s < 256; s++)
|
|
E8[p][s] = E4[p << 1][s & 15] + E4[p << 1 | 1][s >> 4];
|
|
}
|
|
uint best_index = 0;
|
|
for (uint best_error = cUINT32_MAX, s = 0; s < m_color_selectors.size(); s++) {
|
|
uint32 selector = m_color_selectors[s];
|
|
uint error = E8[0][selector & 255] + E8[1][selector >> 8 & 255] + E8[2][selector >> 16 & 255] + E8[3][selector >> 24 & 255];
|
|
if (error < best_error) {
|
|
best_error = error;
|
|
best_index = s;
|
|
}
|
|
}
|
|
uint (&total_errors)[16][4] = selector_details[best_index].error;
|
|
for (uint p = 0; p < 16; p++) {
|
|
for (uint s = 0; s < 4; s++)
|
|
total_errors[p][s] += E2[p][s];
|
|
}
|
|
selector_details[best_index].used = true;
|
|
m_selector_indices[m_has_subblocks ? b << 1 : b].color = best_index;
|
|
}
|
|
}
|
|
|
|
struct SelectorNode {
|
|
uint64 *p, *pEnd;
|
|
SelectorNode (uint64* begin, uint64* end) : p(begin), pEnd(end) {}
|
|
bool operator<(const SelectorNode& other) const { return *p > *other.p; }
|
|
static void sort_task(uint64 data, void* ptr) { std::sort(((SelectorNode*)ptr)->p, ((SelectorNode*)ptr)->pEnd); }
|
|
};
|
|
|
|
void dxt_hc::create_color_selector_codebook() {
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
crnlib::vector<uint64> selectors(m_has_subblocks ? m_num_blocks >> 1 : m_num_blocks);
|
|
for (uint i = 0, b = 0, step = m_has_subblocks ? 2 : 1; b < m_num_blocks; b += step)
|
|
selectors[i++] = m_block_selectors[cColor][b] + (m_has_subblocks ? m_block_selectors[cColor][b + 1] : 0);
|
|
|
|
crnlib::vector<SelectorNode> nodes;
|
|
SelectorNode node(0, selectors.get_ptr());
|
|
for (uint i = 0; i < num_tasks; i++) {
|
|
node.p = node.pEnd;
|
|
node.pEnd = selectors.get_ptr() + selectors.size() * (i + 1) / num_tasks;
|
|
if (node.p != node.pEnd)
|
|
nodes.push_back(node);
|
|
}
|
|
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
m_pTask_pool->queue_task(&SelectorNode::sort_task, i, &nodes[i]);
|
|
m_pTask_pool->join();
|
|
|
|
std::priority_queue<SelectorNode> queue;
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
queue.push(nodes[i]);
|
|
|
|
float v[4];
|
|
for (uint s = 0; s < 4; s++)
|
|
v[s] = (s + 0.5f) * 0.25f;
|
|
|
|
crnlib::vector<vec16F> vectors;
|
|
crnlib::vector<uint> weights;
|
|
vectors.reserve(selectors.size());
|
|
weights.reserve(selectors.size());
|
|
for (uint64 prev_selector = 0; queue.size();) {
|
|
SelectorNode node = queue.top();
|
|
uint64 selector = *node.p++;
|
|
queue.pop();
|
|
if (node.p != node.pEnd)
|
|
queue.push(node);
|
|
uint weight = (uint)selector;
|
|
selector >>= 32;
|
|
if (!vectors.size() || selector != prev_selector) {
|
|
prev_selector = selector;
|
|
vec16F vector;
|
|
for (uint p = 0; p < 16; p++, selector >>= 2)
|
|
vector[15 - p] = v[selector & 3];
|
|
vectors.push_back(vector);
|
|
weights.push_back(weight);
|
|
} else if (weights.back() > UINT_MAX - weight) {
|
|
weights.back() = UINT_MAX;
|
|
} else {
|
|
weights.back() += weight;
|
|
}
|
|
}
|
|
|
|
tree_clusterizer<vec16F> selector_vq;
|
|
selector_vq.generate_codebook(vectors.get_ptr(), weights.get_ptr(), vectors.size(), m_params.m_color_selector_codebook_size, false, m_pTask_pool);
|
|
m_color_selectors.resize(selector_vq.get_codebook_size());
|
|
m_color_selectors_used.resize(selector_vq.get_codebook_size());
|
|
for (uint i = 0; i < selector_vq.get_codebook_size(); i++) {
|
|
const vec16F& v = selector_vq.get_codebook_entry(i);
|
|
m_color_selectors[i] = 0;
|
|
for (uint sh = 0, j = 0; j < 16; j++, sh += 2)
|
|
m_color_selectors[i] |= (uint)(v[j] * 4.0f) << sh;
|
|
}
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|
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crnlib::vector<crnlib::vector<color_selector_details> > selector_details(num_tasks);
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for (uint t = 0; t < num_tasks; t++) {
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selector_details[t].resize(m_color_selectors.size());
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m_pTask_pool->queue_object_task(this, &dxt_hc::create_color_selector_codebook_task, t, &selector_details[t]);
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}
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m_pTask_pool->join();
|
|
|
|
for (uint t = 1; t < num_tasks; t++) {
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|
for (uint i = 0; i < m_color_selectors.size(); i++) {
|
|
for (uint8 p = 0; p < 16; p++) {
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|
for (uint8 s = 0; s < 4; s++)
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|
selector_details[0][i].error[p][s] += selector_details[t][i].error[p][s];
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}
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selector_details[0][i].used = selector_details[0][i].used || selector_details[t][i].used;
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}
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}
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|
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for (uint i = 0; i < m_color_selectors.size(); i++) {
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|
m_color_selectors_used[i] = selector_details[0][i].used;
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uint (&errors)[16][4] = selector_details[0][i].error;
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|
m_color_selectors[i] = 0;
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|
for (uint sh = 0, p = 0; p < 16; p++, sh += 2) {
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|
uint* e = errors[p];
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|
uint8 s03 = e[3] < e[0] ? 3 : 0;
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|
uint8 s12 = e[2] < e[1] ? 2 : 1;
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m_color_selectors[i] |= (e[s12] < e[s03] ? s12 : s03) << sh;
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}
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}
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|
}
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|
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struct alpha_selector_details {
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alpha_selector_details() { utils::zero_object(*this); }
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uint error[16][8];
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|
bool used;
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};
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|
|
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void dxt_hc::create_alpha_selector_codebook_task(uint64 data, void* pData_ptr) {
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|
crnlib::vector<alpha_selector_details>& selector_details = *static_cast<crnlib::vector<alpha_selector_details>*>(pData_ptr);
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
uint E3[16][8];
|
|
uint E6[8][64];
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|
for (uint n = m_has_subblocks ? m_num_blocks >> 1 : m_num_blocks, b = n * data / num_tasks, bEnd = n * (data + 1) / num_tasks; b < bEnd; b++) {
|
|
for (uint c = cAlpha0; c < cAlpha0 + m_num_alpha_blocks; c++) {
|
|
const uint alpha_pixel_comp = m_params.m_alpha_component_indices[c - cAlpha0];
|
|
alpha_cluster& cluster = m_alpha_clusters[m_endpoint_indices[m_has_subblocks ? b << 1 : b].component[c]];
|
|
uint* block_values = cluster.alpha_values;
|
|
for (uint p = 0; p < 16; p++) {
|
|
for (uint s = 0; s < 8; s++) {
|
|
int delta = m_blocks[b][p][alpha_pixel_comp] - block_values[s];
|
|
E3[p][s] = delta * delta;
|
|
}
|
|
}
|
|
for (uint p = 0; p < 8; p++) {
|
|
for (uint s = 0; s < 64; s++)
|
|
E6[p][s] = E3[p << 1][s & 7] + E3[p << 1 | 1][s >> 3];
|
|
}
|
|
uint best_index = 0;
|
|
for (uint best_error = cUINT32_MAX, s = 0; s < m_alpha_selectors.size(); s++) {
|
|
uint64 selector = m_alpha_selectors[s];
|
|
uint error = E6[0][selector & 63];
|
|
error += E6[1][selector >> 6 & 63];
|
|
error += E6[2][selector >> 12 & 63];
|
|
error += E6[3][selector >> 18 & 63];
|
|
error += E6[4][selector >> 24 & 63];
|
|
error += E6[5][selector >> 30 & 63];
|
|
error += E6[6][selector >> 36 & 63];
|
|
error += E6[7][selector >> 42 & 63];
|
|
if (error < best_error) {
|
|
best_error = error;
|
|
best_index = s;
|
|
}
|
|
}
|
|
if (cluster.refined_alpha) {
|
|
block_values = cluster.refined_alpha_values;
|
|
for (uint p = 0; p < 16; p++) {
|
|
for (uint s = 0; s < 8; s++) {
|
|
int delta = m_blocks[b][p][alpha_pixel_comp] - block_values[s];
|
|
E3[p][s] = delta * delta;
|
|
}
|
|
}
|
|
}
|
|
uint (&total_errors)[16][8] = selector_details[best_index].error;
|
|
for (uint p = 0; p < 16; p++) {
|
|
for (uint s = 0; s < 8; s++)
|
|
total_errors[p][s] += E3[p][s];
|
|
}
|
|
selector_details[best_index].used = true;
|
|
m_selector_indices[m_has_subblocks ? b << 1 : b].component[c] = best_index;
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::create_alpha_selector_codebook() {
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
crnlib::vector<uint64> selectors(m_num_alpha_blocks * (m_has_subblocks ? m_num_blocks >> 1 : m_num_blocks));
|
|
for (uint i = 0, c = cAlpha0; c < cAlpha0 + m_num_alpha_blocks; c++) {
|
|
for (uint b = 0, step = m_has_subblocks ? 2 : 1; b < m_num_blocks; b += step)
|
|
selectors[i++] = m_block_selectors[c][b];
|
|
}
|
|
|
|
crnlib::vector<SelectorNode> nodes;
|
|
SelectorNode node(0, selectors.get_ptr());
|
|
for (uint i = 0; i < num_tasks; i++) {
|
|
node.p = node.pEnd;
|
|
node.pEnd = selectors.get_ptr() + selectors.size() * (i + 1) / num_tasks;
|
|
if (node.p != node.pEnd)
|
|
nodes.push_back(node);
|
|
}
|
|
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
m_pTask_pool->queue_task(&SelectorNode::sort_task, i, &nodes[i]);
|
|
m_pTask_pool->join();
|
|
|
|
std::priority_queue<SelectorNode> queue;
|
|
for (uint i = 0; i < nodes.size(); i++)
|
|
queue.push(nodes[i]);
|
|
|
|
float v[8];
|
|
for (uint s = 0; s < 8; s++)
|
|
v[s] = (s + 0.5f) * 0.125f;
|
|
|
|
crnlib::vector<vec16F> vectors;
|
|
crnlib::vector<uint> weights;
|
|
vectors.reserve(selectors.size());
|
|
weights.reserve(selectors.size());
|
|
for (uint64 prev_selector = 0; queue.size();) {
|
|
SelectorNode node = queue.top();
|
|
uint64 selector = *node.p++;
|
|
queue.pop();
|
|
if (node.p != node.pEnd)
|
|
queue.push(node);
|
|
uint weight = (uint16)selector;
|
|
selector >>= 16;
|
|
if (!vectors.size() || selector != prev_selector) {
|
|
prev_selector = selector;
|
|
vec16F vector;
|
|
for (uint p = 0; p < 16; p++, selector >>= 3)
|
|
vector[15 - p] = v[selector & 7];
|
|
vectors.push_back(vector);
|
|
weights.push_back(weight);
|
|
} else if (weights.back() > UINT_MAX - weight) {
|
|
weights.back() = UINT_MAX;
|
|
} else {
|
|
weights.back() += weight;
|
|
}
|
|
}
|
|
|
|
tree_clusterizer<vec16F> selector_vq;
|
|
selector_vq.generate_codebook(vectors.get_ptr(), weights.get_ptr(), vectors.size(), m_params.m_alpha_selector_codebook_size, false, m_pTask_pool);
|
|
m_alpha_selectors.resize(selector_vq.get_codebook_size());
|
|
m_alpha_selectors_used.resize(selector_vq.get_codebook_size());
|
|
for (uint i = 0; i < selector_vq.get_codebook_size(); i++) {
|
|
const vec16F& v = selector_vq.get_codebook_entry(i);
|
|
m_alpha_selectors[i] = 0;
|
|
for (uint sh = 0, j = 0; j < 16; j++, sh += 3)
|
|
m_alpha_selectors[i] |= (uint64)(v[j] * 8.0f) << sh;
|
|
}
|
|
|
|
crnlib::vector<crnlib::vector<alpha_selector_details> > selector_details(num_tasks);
|
|
for (uint t = 0; t < num_tasks; t++) {
|
|
selector_details[t].resize(m_alpha_selectors.size());
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::create_alpha_selector_codebook_task, t, &selector_details[t]);
|
|
}
|
|
m_pTask_pool->join();
|
|
|
|
for (uint t = 1; t < num_tasks; t++) {
|
|
for (uint i = 0; i < m_alpha_selectors.size(); i++) {
|
|
for (uint8 p = 0; p < 16; p++) {
|
|
for (uint8 s = 0; s < 8; s++)
|
|
selector_details[0][i].error[p][s] += selector_details[t][i].error[p][s];
|
|
}
|
|
selector_details[0][i].used = selector_details[0][i].used || selector_details[t][i].used;
|
|
}
|
|
}
|
|
|
|
for (uint i = 0; i < m_alpha_selectors.size(); i++) {
|
|
m_alpha_selectors_used[i] = selector_details[0][i].used;
|
|
uint (&errors)[16][8] = selector_details[0][i].error;
|
|
m_alpha_selectors[i] = 0;
|
|
for (uint sh = 0, p = 0; p < 16; p++, sh += 3) {
|
|
uint* e = errors[p];
|
|
uint8 s07 = e[7] < e[0] ? 7 : 0;
|
|
uint8 s12 = e[2] < e[1] ? 2 : 1;
|
|
uint8 s34 = e[4] < e[3] ? 4 : 3;
|
|
uint8 s56 = e[6] < e[5] ? 6 : 5;
|
|
uint8 s02 = e[s12] < e[s07] ? s12 : s07;
|
|
uint8 s36 = e[s56] < e[s34] ? s56 : s34;
|
|
m_alpha_selectors[i] |= (uint64)(e[s36] < e[s02] ? s36 : s02) << sh;
|
|
}
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::update_progress(uint phase_index, uint subphase_index, uint subphase_total) {
|
|
CRNLIB_ASSERT(crn_get_current_thread_id() == m_main_thread_id);
|
|
|
|
if (!m_params.m_pProgress_func)
|
|
return true;
|
|
|
|
const int percentage_complete = (subphase_total > 1) ? ((100 * subphase_index) / (subphase_total - 1)) : 100;
|
|
if (((int)phase_index == m_prev_phase_index) && (m_prev_percentage_complete == percentage_complete))
|
|
return !m_canceled;
|
|
|
|
m_prev_percentage_complete = percentage_complete;
|
|
|
|
bool status = (*m_params.m_pProgress_func)(phase_index, cTotalCompressionPhases, subphase_index, subphase_total, m_params.m_pProgress_func_data) != 0;
|
|
if (!status) {
|
|
m_canceled = true;
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
} // namespace crnlib
|