Alexander Suvorov
e972e0b480
This change improves compression ratio for ETC1 encoding.
Explanation:
When computing endpoint weights for ETC1 encoding, it is possible to use delta luma instead of the Euclidean distance between the outer endpoint colors, as it gives approximately the same result.
When computing selector weight, it is important to take into account the following factors:
- The bigger is the difference between the outer endpoint colors, the bigger error can be introduced by the corresponding selector, therefore the bigger should be the weight of that selector. In the original Crunch algorithm, the selector weight is proportional to the squared distance between the outer endpoint colors. Such optimization improves PSNR, but it might also introduce significant distortion in smooth areas of the image. In order to mitigate this effect, it is proposed to limit the maximum difference between the endpoint colors (currently delta luma is limited by 100).
- Blocks with low difference between the outer endpoint colors introduce relatively small error, so their selectors should have smaller weights. In the original algorithm it is achieved by using squared distance between the outer endpoint colors, though the effect can be amplified further by using powers higher than 2 (currently it is set to 2.7), which improves PSNR.
In the original Crunch algorithm the encoding weights are initialized non-symmetrically (and are set to math::lerp(1.15f, 1.0f, 1.0f / 7.0f) for horizontal split and to math::lerp(1.15f, 1.0f, 2.0f / 7.0f) for vertical split). It is proposed to use the same encoding weight for both splits in case of ETC1 (the used coefficient 0.972 has been computed as math::lerp(1.15f, 1.0f, 1.5f / 7.0f) / 1.15f).
The ETC1 quantization parameters have been adjusted accordingly to preserve the average Luma 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 / 28.843 sec
Modified: 1473711 bytes / 13.312 sec
Improvement: 6.86% (compression ratio) / 53.85% (compression time)
[Compressing Kodak set with mipmaps using DXT1 encoding]
Original: 2065243 bytes / 36.962 sec
Modified: 1920600 bytes / 18.122 sec
Improvement: 7.00% (compression ratio) / 50.97% (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: 1612083 bytes
Total time: 17.351 sec
Average bitrate: 1.367 bpp
Average Luma PSNR: 34.050 dB
1065 lines
45 KiB
C++
1065 lines
45 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_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_color_blocks = p.m_format == cDXT1 || p.m_format == cDXT5 || p.m_format == cETC1;
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m_num_alpha_blocks = p.m_format == cDXT5 || p.m_format == cDXT5A ? 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_params.m_format == cETC1 ? &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_params.m_format == cETC1 ? 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_params.m_format == cETC1 && 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_params.m_format == cETC1 && (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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void dxt_hc::determine_tiles_task(uint64 data, void* pData_ptr) {
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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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tree_clusterizer<vec3F> color_palettizer;
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tree_clusterizer<vec1F> alpha_palettizer;
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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_color_blocks) {
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uint low16, high16;
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dxt_fast::compress_color_block(size, pixels, low16, high16, selectors);
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color_quad_u8 block_colors[4];
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dxt1_block::get_block_colors4(block_colors, low16, high16);
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uint error = 0;
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for (uint p = 0; p < size; p++) {
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for (uint8 c = 0; c < 3; c++) {
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uint delta = pixels[p][c] - block_colors[selectors[p]][c];
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error += delta * delta;
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}
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}
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tile_error[cColor][t] = error;
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}
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for (uint a = 0; a < m_num_alpha_blocks; a++) {
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uint8 component = m_params.m_alpha_component_indices[a];
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dxt5_endpoint_optimizer optimizer;
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dxt5_endpoint_optimizer::params params;
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dxt5_endpoint_optimizer::results results;
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params.m_pPixels = pixels;
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params.m_num_pixels = size;
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params.m_comp_index = component;
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params.m_use_both_block_types = false;
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params.m_quality = cCRNDXTQualityNormal;
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results.m_pSelectors = selectors;
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optimizer.compute(params, results);
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uint block_values[cDXT5SelectorValues];
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dxt5_block::get_block_values8(block_values, results.m_first_endpoint, results.m_second_endpoint);
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tile_error[cAlpha0 + a][t] = results.m_error;
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}
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}
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for (uint8 c = m_has_color_blocks ? 0 : cAlpha0; c < cAlpha0 + m_num_alpha_blocks; c++) {
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for (uint8 e = 0; e < 8; e++) {
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total_error[c][e] = 0;
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for (uint8 t = 0, s = e + 1; s; s >>= 1, t++)
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total_error[c][e] += tile_error[c][tiles[e][t]];
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}
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}
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float best_quality = 0.0f;
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uint best_encoding = 0;
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for (uint e = 0; e < 8; e++) {
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float quality = 0;
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if (m_has_color_blocks) {
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double peakSNR = total_error[cColor][e] ? log10(255.0f / sqrt(total_error[cColor][e] / 192.0)) * 20.0f : 999999.0f;
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quality = (float)math::maximum<double>(peakSNR - m_color_derating[level][e], 0.0f);
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if (m_num_alpha_blocks)
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quality *= m_params.m_adaptive_tile_color_alpha_weighting_ratio;
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}
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for (uint a = 0; a < m_num_alpha_blocks; a++) {
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double peakSNR = total_error[cAlpha0 + a][e] ? log10(255.0f / sqrt(total_error[cAlpha0 + a][e] / 64.0)) * 20.0f : 999999.0f;
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quality += (float)math::maximum<double>(peakSNR - m_alpha_derating[e], 0.0f);
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}
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if (quality > best_quality) {
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best_quality = quality;
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best_encoding = e;
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}
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}
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for (uint tile_index = 0, s = best_encoding + 1; s; s >>= 1, tile_index++) {
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tile_details& tile = m_tiles[tile_offset | tile_index];
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uint t = tiles[best_encoding][tile_index];
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tile.pixels.append(tilePixels + offsets[t], 16 << (t >> 2));
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tile.weight = weight;
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if (m_has_color_blocks) {
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color_palettizer.clear();
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for (uint p = 0; p < tile.pixels.size(); p++) {
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const color_quad_u8& pixel = tile.pixels[p];
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vec3F v(m_uint8_to_float[pixel[0]], m_uint8_to_float[pixel[1]], m_uint8_to_float[pixel[2]]);
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color_palettizer.add_training_vec(m_params.m_perceptual ? vec3F(v[0] * 0.5f, v[1], v[2] * 0.25f): v, 1);
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}
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color_palettizer.generate_codebook(2);
|
|
bool single = color_palettizer.get_codebook_size() == 1;
|
|
bool reorder = !single && color_palettizer.get_codebook_entry(0).length() > color_palettizer.get_codebook_entry(1).length();
|
|
for (uint t = 0, i = 0; i < 2; i++) {
|
|
vec3F v = color_palettizer.get_codebook_entry(single ? 0 : reorder ? 1 - i : i);
|
|
for (uint c = 0; c < 3; c++, t++)
|
|
tile.color_endpoint[t] = v[c];
|
|
}
|
|
}
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
alpha_palettizer.clear();
|
|
for (uint c = m_params.m_alpha_component_indices[a], p = 0; p < tile.pixels.size(); p++)
|
|
alpha_palettizer.add_training_vec(vec1F(m_uint8_to_float[tile.pixels[p][c]]), 1);
|
|
alpha_palettizer.generate_codebook(2);
|
|
float v[2] = {alpha_palettizer.get_codebook_entry(0)[0], alpha_palettizer.get_codebook_entry(alpha_palettizer.get_codebook_size() - 1)[0]};
|
|
tile.alpha_endpoints[a][0] = math::minimum(v[0], v[1]);
|
|
tile.alpha_endpoints[a][1] = math::maximum(v[0], v[1]);
|
|
}
|
|
}
|
|
|
|
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* pData_ptr) {
|
|
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];
|
|
tree_clusterizer<vec3F> color_palettizer;
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
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;
|
|
color_palettizer.clear();
|
|
for (uint p = 0; p < tile.pixels.size(); p++) {
|
|
const color_quad_u8& pixel = tile.pixels[p];
|
|
vec3F v(m_uint8_to_float[pixel[0]], m_uint8_to_float[pixel[1]], m_uint8_to_float[pixel[2]]);
|
|
color_palettizer.add_training_vec(m_params.m_perceptual ? vec3F(v[0] * 0.5f, v[1], v[2] * 0.25f) : v, 1);
|
|
}
|
|
color_palettizer.generate_codebook(2);
|
|
bool single = color_palettizer.get_codebook_size() == 1;
|
|
bool reorder = !single && color_palettizer.get_codebook_entry(0).length() > color_palettizer.get_codebook_entry(1).length();
|
|
for (uint t = 0, i = 0; i < 2; i++) {
|
|
vec3F v = color_palettizer.get_codebook_entry(single ? 0 : reorder ? 1 - i : i);
|
|
for (uint c = 0; c < 3; c++, t++)
|
|
tile.color_endpoint[t] = v[c];
|
|
}
|
|
}
|
|
|
|
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* pData_ptr) {
|
|
pData_ptr;
|
|
const uint thread_index = static_cast<uint>(data);
|
|
|
|
if (!m_has_color_blocks)
|
|
return;
|
|
|
|
uint total_empty_clusters = 0;
|
|
for (uint cluster_index = 0; cluster_index < m_color_clusters.size(); cluster_index++) {
|
|
if (m_canceled)
|
|
return;
|
|
|
|
if ((crn_get_current_thread_id() == m_main_thread_id) && ((cluster_index & 63) == 0)) {
|
|
if (!update_progress(3, cluster_index, m_color_clusters.size()))
|
|
return;
|
|
}
|
|
|
|
if (m_pTask_pool->get_num_threads()) {
|
|
if ((cluster_index % (m_pTask_pool->get_num_threads() + 1)) != thread_index)
|
|
continue;
|
|
}
|
|
|
|
color_cluster& cluster = m_color_clusters[cluster_index];
|
|
if (cluster.pixels.empty())
|
|
continue;
|
|
|
|
crnlib::vector<uint8> selectors(cluster.pixels.size());
|
|
|
|
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;
|
|
results.m_pSelectors = selectors.get_ptr();
|
|
|
|
dxt1_endpoint_optimizer optimizer;
|
|
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 sh = 0, p = 0; p < 16; p++, sh += 2) {
|
|
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 |= s_best << sh;
|
|
}
|
|
m_block_selectors[cColor][b] = selector | (uint64)weight << 32;
|
|
}
|
|
|
|
dxt_endpoint_refiner refiner;
|
|
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* pData_ptr) {
|
|
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];
|
|
for (uint i = 0; i < blocks.size(); i++) {
|
|
uint b = blocks[i];
|
|
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 sh = 0, p = 0; p < 8; p++, sh += 2) {
|
|
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, ((color_quad_u8(*)[8])m_blocks)[b][p], cluster.color_values[s], false);
|
|
if (error < error_best) {
|
|
s_best = s;
|
|
error_best = error;
|
|
}
|
|
}
|
|
selector |= s_best << sh;
|
|
}
|
|
m_block_selectors[cColor][b] = selector | (uint64)weight << 32;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoint_clusters_task(uint64 data, void* pData_ptr) {
|
|
tree_clusterizer<vec6F>* vq = (tree_clusterizer<vec6F>*)pData_ptr;
|
|
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())
|
|
m_tiles[t].cluster_indices[cColor] = vq->find_best_codebook_entry_fs(m_tiles[t].color_endpoint);
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoints() {
|
|
tree_clusterizer<vec6F> vq;
|
|
for (uint t = 0; t < m_tiles.size(); t++) {
|
|
if (m_tiles[t].pixels.size())
|
|
vq.add_training_vec(m_tiles[t].color_endpoint, (uint)(m_tiles[t].pixels.size() * m_tiles[t].weight));
|
|
}
|
|
|
|
vq.generate_codebook(math::minimum<uint>(m_num_tiles, m_params.m_color_endpoint_codebook_size));
|
|
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_params.m_format == cETC1 && 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_params.m_format == cETC1 ? &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* pData_ptr) {
|
|
pData_ptr;
|
|
const uint thread_index = static_cast<uint>(data);
|
|
|
|
for (uint cluster_index = 0; cluster_index < m_alpha_clusters.size(); cluster_index++) {
|
|
if (m_canceled)
|
|
return;
|
|
|
|
if ((crn_get_current_thread_id() == m_main_thread_id) && ((cluster_index & 63) == 0)) {
|
|
if (!update_progress(8, cluster_index, m_alpha_clusters.size()))
|
|
return;
|
|
}
|
|
|
|
if (m_pTask_pool->get_num_threads()) {
|
|
if ((cluster_index % (m_pTask_pool->get_num_threads() + 1)) != thread_index)
|
|
continue;
|
|
}
|
|
|
|
alpha_cluster& cluster = m_alpha_clusters[cluster_index];
|
|
if (cluster.pixels.empty())
|
|
continue;
|
|
|
|
crnlib::vector<uint8> selectors(cluster.pixels.size());
|
|
|
|
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;
|
|
results.m_pSelectors = selectors.get_ptr();
|
|
|
|
dxt5_endpoint_optimizer optimizer;
|
|
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.second_endpoint - cluster.first_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));
|
|
|
|
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 sh = 0, p = 0; p < 16; p++, sh += 3) {
|
|
uint error_best = cUINT32_MAX;
|
|
uint8 s_best = 0;
|
|
for (uint8 t = 0; t < 8; t++) {
|
|
uint8 s = results.m_reordered ? 7 - g_dxt5_to_linear[t] : g_dxt5_to_linear[t];
|
|
int delta = m_blocks[b][p][component_index] - alpha_values[s];
|
|
uint error = delta >= 0 ? delta : -delta;
|
|
if (error < error_best) {
|
|
s_best = s;
|
|
error_best = error;
|
|
}
|
|
}
|
|
selector |= (uint64)s_best << sh;
|
|
}
|
|
m_block_selectors[cAlpha0 + a][b] = selector | (uint64)weight << 48;
|
|
}
|
|
}
|
|
|
|
dxt_endpoint_refiner refiner;
|
|
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 = 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;
|
|
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++)
|
|
m_tiles[t].cluster_indices[cAlpha0 + a] = vq->find_best_codebook_entry_fs(m_tiles[t].alpha_endpoints[a]);
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::determine_alpha_endpoints() {
|
|
tree_clusterizer<vec2F> vq;
|
|
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())
|
|
vq.add_training_vec(m_tiles[t].alpha_endpoints[a], m_tiles[t].pixels.size());
|
|
}
|
|
}
|
|
|
|
vq.generate_codebook(math::minimum<uint>(m_num_tiles, m_params.m_alpha_endpoint_codebook_size));
|
|
m_alpha_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_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;
|
|
m_alpha_clusters[cluster_index].blocks[cAlpha0 + a].push_back(b);
|
|
}
|
|
}
|
|
|
|
for (uint i = 0; i <= m_pTask_pool->get_num_threads(); 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_params.m_format == cETC1 ? 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_params.m_format == cETC1 ? 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_params.m_format == cETC1 ? b << 1 : b].color = best_index;
|
|
}
|
|
}
|
|
|
|
void dxt_hc::create_color_selector_codebook() {
|
|
tree_clusterizer<vec16F> selector_vq;
|
|
vec16F v;
|
|
for (uint n = m_params.m_format == cETC1 ? m_num_blocks >> 1 : m_num_blocks, b = 0; b < n; b++) {
|
|
uint64 selector = m_params.m_format == cETC1 ? m_block_selectors[cColor][b << 1] | m_block_selectors[cColor][b << 1 | 1] << 16 : m_block_selectors[cColor][b];
|
|
for (uint8 p = 0; p < 16; p++, selector >>= 2)
|
|
v[p] = ((selector & 3) + 0.5f) * 0.25f;
|
|
selector_vq.add_training_vec(v, m_params.m_format == cETC1 ? (selector & 0xFFFF) + (selector >> 16) : selector);
|
|
}
|
|
selector_vq.generate_codebook(m_params.m_color_selector_codebook_size);
|
|
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;
|
|
}
|
|
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
crnlib::vector<crnlib::vector<color_selector_details>> selector_details(num_tasks);
|
|
for (uint t = 0; t < num_tasks; t++) {
|
|
selector_details[t].resize(m_color_selectors.size());
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::create_color_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_color_selectors.size(); i++) {
|
|
for (uint8 p = 0; p < 16; p++) {
|
|
for (uint8 s = 0; s < 4; 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_color_selectors.size(); i++) {
|
|
m_color_selectors_used[i] = selector_details[0][i].used;
|
|
uint (&errors)[16][4] = selector_details[0][i].error;
|
|
m_color_selectors[i] = 0;
|
|
for (uint sh = 0, p = 0; p < 16; p++, sh += 2) {
|
|
uint* e = errors[p];
|
|
uint8 s03 = e[3] < e[0] ? 3 : 0;
|
|
uint8 s12 = e[2] < e[1] ? 2 : 1;
|
|
m_color_selectors[i] |= (e[s12] < e[s03] ? s12 : s03) << sh;
|
|
}
|
|
}
|
|
}
|
|
|
|
struct alpha_selector_details {
|
|
alpha_selector_details() { utils::zero_object(*this); }
|
|
uint error[16][8];
|
|
bool used;
|
|
};
|
|
|
|
void dxt_hc::create_alpha_selector_codebook_task(uint64 data, void* pData_ptr) {
|
|
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];
|
|
for (uint b = m_num_blocks * data / num_tasks, bEnd = m_num_blocks * (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[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[b].component[c] = best_index;
|
|
}
|
|
}
|
|
}
|
|
|
|
void dxt_hc::create_alpha_selector_codebook() {
|
|
tree_clusterizer<vec16F> selector_vq;
|
|
vec16F v;
|
|
for (uint c = cAlpha0; c < cAlpha0 + m_num_alpha_blocks; c++) {
|
|
for (uint b = 0; b < m_num_blocks; b++) {
|
|
uint64 selector = m_block_selectors[c][b];
|
|
for (uint8 p = 0; p < 16; p++, selector >>= 3)
|
|
v[p] = ((selector & 7) + 0.5f) * 0.125f;
|
|
selector_vq.add_training_vec(v, selector);
|
|
}
|
|
}
|
|
selector_vq.generate_codebook(m_params.m_alpha_selector_codebook_size);
|
|
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;
|
|
}
|
|
|
|
uint num_tasks = m_pTask_pool->get_num_threads() + 1;
|
|
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
|