Alexander Suvorov
a14a313361
This change improves the compression speed for DXT encoding.
Explanation:
In order to evaluate an endpoint solution, it is necessary to compute the sum of the squared distances from the source pixels to their nearest block colors, defined by the evaluated endpoint solution. Such computation is quite complicated, so before it is performed, we can compute the sum of the squared distances from the source pixels to the axis-aligned bounding box enclosing all the evaluated block colors (if the source pixel appears to be inside the AABB of the evaluated solution, then the distance is considered to be 0). If the sum of the squared distances to the AABB of the current solution is already bigger than the sum of the squared distances computed for the previously found best solution, then the current solution does not need to be evaluated.
The actual trick here is that the sum of the squared distances to the AABB of the current solution can be computed in constant time using the following approach. The sums of the squared distances for each color component can be computed separately. For each color component the AABB determines 2 planes: the "lower" plane, defined by the lower boundary of the AABB, and the "upper" plane, defined by the upper boundary of the AABB. The sum for each color component is combined from two parts: the sum of the squared distances from the lower plane to all the source pixels which are below the lower plane, and the sum of the squared distances from the upper plane to all the source pixels which are above the upper plane. Considering that the endpoints of the evaluated solution are encoded as RGB565, there are 32 possible planes for the red and blue components, and 64 possible planes for the green component. For each plane it is sufficient to precompute the following two values: the sum of the squared distances from the plane to all the source pixels which are "below" this plane, and the sum of the squared distances from the plane to all the source pixels which are "above" this plane. The total sum of the squared distances from the source pixels to any evaluated AABB can then be represented as a sum of 6 precomputed values, while all the used values can be precomputed in linear time with dynamic programming.
Note: The AABB check seems to work faster than inserting a solution into the hash map. For this reason the AABB check is performed first.
Additional improvements: A few minor adjustments have been made in order to make sure that the texture decompression gives identical result to the original version of Crunch also for 32-bit builds (original Crunch library uses different floating point models for 32-bit and 64-bit builds).
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 / 28.861 sec
Modified: 1468204 bytes / 8.622 sec
Improvement: 7.21% (compression ratio) / 70.13% (compression time)
[Compressing Kodak set with mipmaps using DXT1 encoding]
Original: 2065243 bytes / 36.980 sec
Modified: 1914805 bytes / 11.294 sec
Improvement: 7.28% (compression ratio) / 69.46% (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: 15.529 sec
Average bitrate: 1.363 bpp
Average Luma PSNR: 34.050 dB
1104 lines
47 KiB
C++
1104 lines
47 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_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;
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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 ? 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_etc_color_blocks ? &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_etc_color_blocks && 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_etc_color_blocks && 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*) {
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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();
|
|
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 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*) {
|
|
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;
|
|
tree_clusterizer<vec1F> alpha_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;
|
|
}
|
|
}
|
|
|
|
vec2F alpha_endpoints;
|
|
if (m_num_alpha_blocks) {
|
|
alpha_palettizer.clear();
|
|
for (uint p = 0; p < 16; p++)
|
|
alpha_palettizer.add_training_vec(vec1F(m_uint8_to_float[tilePixels[p].a]), 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]};
|
|
alpha_endpoints[0] = math::minimum(v[0], v[1]);
|
|
alpha_endpoints[1] = math::maximum(v[0], v[1]);
|
|
}
|
|
|
|
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];
|
|
}
|
|
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 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::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];
|
|
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;
|
|
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() {
|
|
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), true);
|
|
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_etc_color_blocks && 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 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 = 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_etc_color_blocks ? 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 |= (uint64)s_best << sh;
|
|
}
|
|
m_block_selectors[cAlpha0 + a][b] = selector | (uint64)weight << 48;
|
|
}
|
|
}
|
|
|
|
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() {
|
|
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;
|
|
if (!(m_has_etc_color_blocks && b & 1))
|
|
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_has_etc_color_blocks ? 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_etc_color_blocks ? 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_etc_color_blocks ? b << 1 : b].color = best_index;
|
|
}
|
|
}
|
|
|
|
void dxt_hc::create_color_selector_codebook() {
|
|
tree_clusterizer<vec16F> selector_vq;
|
|
vec16F v;
|
|
for (uint n = m_has_etc_color_blocks ? m_num_blocks >> 1 : m_num_blocks, b = 0; b < n; b++) {
|
|
uint64 selector = m_has_etc_color_blocks ? 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_has_etc_color_blocks ? (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 n = m_has_etc_color_blocks ? 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_etc_color_blocks ? 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_etc_color_blocks ? b << 1 : 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 += m_has_etc_color_blocks ? 2 : 1) {
|
|
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];
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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_alpha_selectors.size(); i++) {
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m_alpha_selectors_used[i] = selector_details[0][i].used;
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uint (&errors)[16][8] = selector_details[0][i].error;
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m_alpha_selectors[i] = 0;
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for (uint sh = 0, p = 0; p < 16; p++, sh += 3) {
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uint* e = errors[p];
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uint8 s07 = e[7] < e[0] ? 7 : 0;
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uint8 s12 = e[2] < e[1] ? 2 : 1;
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uint8 s34 = e[4] < e[3] ? 4 : 3;
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uint8 s56 = e[6] < e[5] ? 6 : 5;
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uint8 s02 = e[s12] < e[s07] ? s12 : s07;
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uint8 s36 = e[s56] < e[s34] ? s56 : s34;
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m_alpha_selectors[i] |= (uint64)(e[s36] < e[s02] ? s36 : s02) << sh;
|
|
}
|
|
}
|
|
}
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|
|
|
bool dxt_hc::update_progress(uint phase_index, uint subphase_index, uint subphase_total) {
|
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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;
|
|
}
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|
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} // namespace crnlib
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