Alexander Suvorov
cd9ba9b615
This change improves compression speed and simplifies further modification of the code. Explanation: This change is required for further optimization of the tile computation code. Additional performance boost is achieved by moving the tile palettizing into the tile computation thread. 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. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.928 sec Modified: 1494501 bytes / 18.259 sec Improvement: 5.54% (compression ratio) / 36.88% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.978 sec Modified: 1945365 bytes / 23.857 sec Improvement: 5.80% (compression ratio) / 35.48% (compression time)
1268 lines
48 KiB
C++
1268 lines
48 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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#define CRNLIB_USE_FAST_DXT 1
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#define CRNLIB_ENABLE_DEBUG_MESSAGES 0
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namespace crnlib {
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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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static color_quad_u8 g_tile_layout_colors[cNumChunkTileLayouts] =
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{
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color_quad_u8(255, 90, 32, 255),
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color_quad_u8(64, 210, 192, 255),
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color_quad_u8(128, 16, 225, 255),
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color_quad_u8(255, 192, 200, 255),
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color_quad_u8(255, 128, 200, 255),
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color_quad_u8(255, 0, 0, 255),
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color_quad_u8(0, 255, 0, 255),
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color_quad_u8(0, 0, 255, 255),
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color_quad_u8(255, 0, 255, 255)};
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dxt_hc::dxt_hc()
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: m_num_chunks(0),
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m_pChunks(NULL),
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m_num_alpha_blocks(0),
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m_has_color_blocks(false),
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m_has_alpha0_blocks(false),
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m_has_alpha1_blocks(false),
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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_num_chunks = 0;
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m_pChunks = NULL;
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m_num_alpha_blocks = 0;
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m_has_color_blocks = false;
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m_has_alpha0_blocks = false;
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m_has_alpha1_blocks = false;
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m_color_clusters.clear();
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m_alpha_clusters.clear();
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m_alpha_selectors_vec.clear();
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m_color_selectors_vec.clear();
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m_color_endpoints.clear();
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m_alpha_endpoints.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_chunk_details.clear();
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m_blocks.clear();
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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_total_tiles = 0;
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}
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bool dxt_hc::initialize_blocks(const params& p) {
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m_chunk_details.resize(m_num_chunks);
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m_blocks.resize(m_num_chunks << 2);
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m_block_weights.resize(m_blocks.size());
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m_block_encodings.resize(m_blocks.size());
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for (uint c = 0; c < 3; c++)
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m_block_selectors[c].resize(m_blocks.size());
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m_tile_indices.resize(m_blocks.size());
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m_endpoint_indices.resize(m_blocks.size());
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m_selector_indices.resize(m_blocks.size());
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m_tiles.resize(m_blocks.size());
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for (uint level = 0; level < p.m_num_levels; level++) {
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uint first_chunk = p.m_levels[level].m_first_chunk;
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uint end_chunk = p.m_levels[level].m_first_chunk + p.m_levels[level].m_num_chunks;
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uint chunk_width = p.m_levels[level].m_chunk_width;
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uint block_width = chunk_width << 1;
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for (uint b = first_chunk << 2, cy = 0, chunk_base = first_chunk; chunk_base < end_chunk; chunk_base += chunk_width, cy++) {
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for (uint by = 0; by < 2; by++) {
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for (uint cx = 0; cx < chunk_width; cx++) {
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for (uint bx = 0; bx < 2; bx++, b++) {
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const pixel_chunk& chunk = m_pChunks[chunk_base + cx];
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m_block_weights[b] = chunk.m_weight;
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m_chunk_details[chunk_base + cx].block_index[by][bx] = b;
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for (uint t = 0, y = 0; y < 4; y++) {
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for (uint x = 0; x < 4; x++, t++)
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m_blocks[b].push_back(chunk(bx << 2 | x, by << 2 | y));
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}
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}
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}
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}
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}
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}
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return true;
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}
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bool dxt_hc::compress(const params& p, uint num_chunks, const pixel_chunk* pChunks, task_pool& task_pool) {
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m_pTask_pool = &task_pool;
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m_main_thread_id = crn_get_current_thread_id();
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crnlib::vector<endpoint_indices_details>& endpoint_indices = *p.m_endpoint_indices;
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crnlib::vector<selector_indices_details>& selector_indices = *p.m_selector_indices;
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if ((!num_chunks) || (!pChunks))
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return false;
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if ((m_params.m_format == cDXT1A) || (m_params.m_format == cDXT3))
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return false;
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clear();
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m_params = p;
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m_num_chunks = num_chunks;
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m_pChunks = pChunks;
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switch (m_params.m_format) {
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case cDXT1: {
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m_has_color_blocks = true;
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break;
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}
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case cDXT5: {
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m_has_color_blocks = true;
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m_has_alpha0_blocks = true;
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m_num_alpha_blocks = 1;
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break;
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}
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case cDXT5A: {
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m_has_alpha0_blocks = true;
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m_num_alpha_blocks = 1;
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break;
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}
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case cDXN_XY:
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case cDXN_YX: {
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m_has_alpha0_blocks = true;
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m_has_alpha1_blocks = true;
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m_num_alpha_blocks = 2;
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break;
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}
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default: {
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return false;
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}
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}
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initialize_blocks(p);
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determine_compressed_chunks();
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if (m_has_color_blocks) {
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if (!determine_color_endpoint_clusters())
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return false;
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if (!determine_color_endpoint_codebook())
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return false;
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}
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if (m_num_alpha_blocks) {
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if (!determine_alpha_endpoint_clusters())
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return false;
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if (!determine_alpha_endpoint_codebook())
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return false;
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}
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if (m_has_color_blocks) {
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if (!create_color_selector_codebook())
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return false;
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}
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if (m_num_alpha_blocks) {
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if (!create_alpha_selector_codebook())
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return false;
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}
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crnlib::vector<uint16> color_endpoint_remap(m_color_clusters.size());
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m_color_endpoints.reserve(m_color_clusters.size());
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hash_map<uint, uint> color_clusters_map;
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for (uint i = 0; i < m_color_clusters.size(); i++) {
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if (m_color_clusters[i].m_pixels.size()) {
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uint endpoint = dxt1_block::pack_endpoints(m_color_clusters[i].m_refined_first_endpoint, m_color_clusters[i].m_refined_second_endpoint);
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hash_map<uint, uint>::insert_result insert_result = color_clusters_map.insert(endpoint, m_color_endpoints.size());
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if (insert_result.second) {
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color_endpoint_remap[i] = m_color_endpoints.size();
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m_color_endpoints.push_back(endpoint);
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} else {
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color_endpoint_remap[i] = insert_result.first->second;
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}
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}
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}
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crnlib::vector<uint16> color_selector_remap(m_color_selectors.size());
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m_color_selectors_vec.reserve(m_color_selectors.size());
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hash_map<uint32, uint> color_selector_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_selector_map.insert(m_color_selectors[i], m_color_selectors_vec.size());
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if (insert_result.second) {
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color_selector_remap[i] = m_color_selectors_vec.size();
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selectors selector_vec;
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for (uint32 selector = m_color_selectors[i], s = 0; s < 16; s++, selector >>= 2)
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selector_vec.set_by_index(s, selector & 3);
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m_color_selectors_vec.push_back(selector_vec);
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} else {
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color_selector_remap[i] = insert_result.first->second;
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}
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}
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}
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crnlib::vector<uint16> alpha_endpoint_remap(m_alpha_clusters.size());
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m_alpha_endpoints.reserve(m_alpha_clusters.size());
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hash_map<uint, 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].m_pixels.size()) {
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uint endpoint = dxt5_block::pack_endpoints(m_alpha_clusters[i].m_refined_first_endpoint, m_alpha_clusters[i].m_refined_second_endpoint);
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hash_map<uint, uint>::insert_result insert_result = alpha_endpoints_map.insert(endpoint, m_alpha_endpoints.size());
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if (insert_result.second) {
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alpha_endpoint_remap[i] = m_alpha_endpoints.size();
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m_alpha_endpoints.push_back(endpoint);
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} else {
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alpha_endpoint_remap[i] = insert_result.first->second;
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}
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}
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}
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crnlib::vector<uint16> alpha_selector_remap(m_alpha_selectors.size());
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m_alpha_selectors_vec.reserve(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], m_alpha_selectors_vec.size());
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if (insert_result.second) {
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alpha_selector_remap[i] = m_alpha_selectors_vec.size();
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selectors selector_vec;
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for (uint64 selector = m_alpha_selectors[i], s = 0; s < 16; s++, selector >>= 3)
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selector_vec.set_by_index(s, selector & 7);
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m_alpha_selectors_vec.push_back(selector_vec);
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} else {
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alpha_selector_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_blocks.size());
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selector_indices.resize(m_blocks.size());
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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_chunk << 2;
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uint end_block = first_block + (p.m_levels[level].m_num_chunks << 2);
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uint block_width = p.m_levels[level].m_chunk_width << 1;
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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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for (uint c = m_has_color_blocks ? 0 : cAlpha0Chunks; c < cAlpha0Chunks + m_num_alpha_blocks; c++) {
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uint16 endpoint_index = (c ? alpha_endpoint_remap : color_endpoint_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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endpoint_indices[b].component[c] = endpoint_index;
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uint16 selector_index = (c ? alpha_selector_remap : color_selector_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 = left_match ? 1 : top_match ? 2 : 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::compress_dxt1_block(
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dxt1_endpoint_optimizer::results& results,
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uint chunk_index, const image_u8& chunk, uint x_ofs, uint y_ofs, uint width, uint height,
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uint8* pColor_Selectors) {
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chunk_index;
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color_quad_u8 pixels[cChunkPixelWidth * cChunkPixelHeight];
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for (uint y = 0; y < height; y++)
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for (uint x = 0; x < width; x++)
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pixels[x + y * width] = chunk(x_ofs + x, y_ofs + y);
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//double s = image_utils::compute_std_dev(width * height, pixels, 0, 3);
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#if CRNLIB_USE_FAST_DXT
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uint low16, high16;
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dxt_fast::compress_color_block(width * height, pixels, low16, high16, pColor_Selectors);
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results.m_low_color = static_cast<uint16>(low16);
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results.m_high_color = static_cast<uint16>(high16);
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results.m_alpha_block = false;
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results.m_error = INT_MAX;
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results.m_pSelectors = pColor_Selectors;
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#else
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dxt1_endpoint_optimizer optimizer;
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dxt1_endpoint_optimizer::params params;
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params.m_block_index = chunk_index;
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params.m_pPixels = pixels;
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params.m_num_pixels = width * height;
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params.m_pixels_have_alpha = false;
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params.m_use_alpha_blocks = false;
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params.m_perceptual = m_params.m_perceptual;
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params.m_highest_quality = false; //false;
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params.m_endpoint_caching = false;
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results.m_pSelectors = pColor_Selectors;
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optimizer.compute(params, results);
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#endif
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}
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void dxt_hc::compress_dxt5_block(
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dxt5_endpoint_optimizer::results& results,
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uint chunk_index, const image_u8& chunk, uint x_ofs, uint y_ofs, uint width, uint height, uint component_index,
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uint8* pAlpha_selectors) {
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chunk_index;
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color_quad_u8 pixels[cChunkPixelWidth * cChunkPixelHeight];
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for (uint y = 0; y < height; y++)
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for (uint x = 0; x < width; x++)
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pixels[x + y * width] = chunk(x_ofs + x, y_ofs + y);
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#if 0 //CRNLIB_USE_FAST_DXT
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uint low, high;
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dxt_fast::compress_alpha_block(width * height, pixels, low, high, pAlpha_selectors, component_index);
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results.m_pSelectors = pAlpha_selectors;
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results.m_error = INT_MAX;
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results.m_first_endpoint = static_cast<uint8>(low);
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results.m_second_endpoint = static_cast<uint8>(high);
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results.m_block_type = 0;
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#else
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dxt5_endpoint_optimizer optimizer;
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dxt5_endpoint_optimizer::params params;
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params.m_block_index = chunk_index;
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params.m_pPixels = pixels;
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params.m_num_pixels = width * height;
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params.m_comp_index = component_index;
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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 = pAlpha_selectors;
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optimizer.compute(params, results);
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#endif
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}
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void dxt_hc::determine_compressed_chunks_task(uint64 data, void* pData_ptr) {
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pData_ptr;
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const uint thread_index = static_cast<uint>(data);
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image_u8 orig_chunk;
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image_u8 decomp_chunk[cNumChunkEncodings];
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orig_chunk.resize(cChunkPixelWidth, cChunkPixelHeight);
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for (uint i = 0; i < cNumChunkEncodings; i++)
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decomp_chunk[i].resize(cChunkPixelWidth, cChunkPixelHeight);
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image_utils::error_metrics color_error_metrics[cNumChunkEncodings];
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dxt1_endpoint_optimizer::results color_optimizer_results[cNumChunkTileLayouts];
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uint8 layout_color_selectors[cNumChunkTileLayouts][cChunkPixelWidth * cChunkPixelHeight];
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image_utils::error_metrics alpha_error_metrics[2][cNumChunkEncodings];
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dxt5_endpoint_optimizer::results alpha_optimizer_results[2][cNumChunkTileLayouts];
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uint8 layout_alpha_selectors[2][cNumChunkTileLayouts][cChunkPixelWidth * cChunkPixelHeight];
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uint first_layout = 0;
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uint last_layout = cNumChunkTileLayouts;
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uint first_encoding = 0;
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uint last_encoding = cNumChunkEncodings;
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if (!m_params.m_hierarchical) {
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first_layout = cFirst4x4ChunkTileLayout;
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first_encoding = cNumChunkEncodings - 1;
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}
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float encoding_weight[8];
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for (uint i = 0; i < 8; i++)
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encoding_weight[i] = math::lerp(1.15f, 1.0f, i / 7.0f);
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for (uint chunk_index = 0; chunk_index < m_num_chunks; chunk_index++) {
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if (m_canceled)
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return;
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if ((crn_get_current_thread_id() == m_main_thread_id) && ((chunk_index & 511) == 0)) {
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if (!update_progress(0, chunk_index, m_num_chunks))
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return;
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}
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if (m_pTask_pool->get_num_threads()) {
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if ((chunk_index % (m_pTask_pool->get_num_threads() + 1)) != thread_index)
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continue;
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}
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uint level_index = 0;
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for (uint i = 0; i < m_params.m_num_levels; i++) {
|
|
if ((chunk_index >= m_params.m_levels[i].m_first_chunk) && (chunk_index < m_params.m_levels[i].m_first_chunk + m_params.m_levels[i].m_num_chunks)) {
|
|
level_index = i;
|
|
break;
|
|
}
|
|
}
|
|
|
|
for (uint cy = 0; cy < cChunkPixelHeight; cy++)
|
|
for (uint cx = 0; cx < cChunkPixelWidth; cx++)
|
|
orig_chunk(cx, cy) = m_pChunks[chunk_index](cx, cy);
|
|
|
|
if (m_has_color_blocks) {
|
|
for (uint l = first_layout; l < last_layout; l++) {
|
|
utils::zero_object(layout_color_selectors[l]);
|
|
|
|
compress_dxt1_block(
|
|
color_optimizer_results[l], chunk_index,
|
|
orig_chunk,
|
|
g_chunk_tile_layouts[l].m_x_ofs, g_chunk_tile_layouts[l].m_y_ofs,
|
|
g_chunk_tile_layouts[l].m_width, g_chunk_tile_layouts[l].m_height,
|
|
layout_color_selectors[l]);
|
|
}
|
|
}
|
|
|
|
float alpha_layout_std_dev[2][cNumChunkTileLayouts];
|
|
utils::zero_object(alpha_layout_std_dev);
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
for (uint l = first_layout; l < last_layout; l++) {
|
|
utils::zero_object(layout_alpha_selectors[a][l]);
|
|
|
|
compress_dxt5_block(
|
|
alpha_optimizer_results[a][l], chunk_index,
|
|
orig_chunk,
|
|
g_chunk_tile_layouts[l].m_x_ofs, g_chunk_tile_layouts[l].m_y_ofs,
|
|
g_chunk_tile_layouts[l].m_width, g_chunk_tile_layouts[l].m_height,
|
|
m_params.m_alpha_component_indices[a],
|
|
layout_alpha_selectors[a][l]);
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
float mean = 0.0f;
|
|
float variance = 0.0f;
|
|
|
|
for (uint cy = 0; cy < g_chunk_tile_layouts[l].m_height; cy++) {
|
|
for (uint cx = 0; cx < g_chunk_tile_layouts[l].m_width; cx++) {
|
|
uint s = orig_chunk(cx + g_chunk_tile_layouts[l].m_x_ofs, cy + g_chunk_tile_layouts[l].m_y_ofs)[m_params.m_alpha_component_indices[a]];
|
|
|
|
mean += s;
|
|
variance += s * s;
|
|
} // cx
|
|
} //cy
|
|
|
|
float scale = 1.0f / (g_chunk_tile_layouts[l].m_width * g_chunk_tile_layouts[l].m_height);
|
|
|
|
mean *= scale;
|
|
variance *= scale;
|
|
|
|
variance -= mean * mean;
|
|
|
|
alpha_layout_std_dev[a][l] = sqrt(variance);
|
|
|
|
} //a
|
|
}
|
|
}
|
|
|
|
for (uint e = first_encoding; e < last_encoding; e++) {
|
|
for (uint t = 0; t < g_chunk_encodings[e].m_num_tiles; t++) {
|
|
const uint layout_index = g_chunk_encodings[e].m_tiles[t].m_layout_index;
|
|
CRNLIB_ASSERT((layout_index >= first_layout) && (layout_index < last_layout));
|
|
|
|
if (m_has_color_blocks) {
|
|
const dxt1_endpoint_optimizer::results& color_results = color_optimizer_results[layout_index];
|
|
const uint8* pColor_selectors = layout_color_selectors[layout_index];
|
|
|
|
color_quad_u8 block_colors[cDXT1SelectorValues];
|
|
CRNLIB_ASSERT(color_results.m_low_color >= color_results.m_high_color);
|
|
// it's okay if color_results.m_low_color == color_results.m_high_color, because in this case only selector 0 should be used
|
|
dxt1_block::get_block_colors4(block_colors, color_results.m_low_color, color_results.m_high_color);
|
|
|
|
for (uint cy = 0; cy < g_chunk_encodings[e].m_tiles[t].m_height; cy++) {
|
|
for (uint cx = 0; cx < g_chunk_encodings[e].m_tiles[t].m_width; cx++) {
|
|
uint s = pColor_selectors[cx + cy * g_chunk_encodings[e].m_tiles[t].m_width];
|
|
CRNLIB_ASSERT(s < cDXT1SelectorValues);
|
|
|
|
decomp_chunk[e](cx + g_chunk_encodings[e].m_tiles[t].m_x_ofs, cy + g_chunk_encodings[e].m_tiles[t].m_y_ofs) = block_colors[s];
|
|
}
|
|
}
|
|
}
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
const dxt5_endpoint_optimizer::results& alpha_results = alpha_optimizer_results[a][layout_index];
|
|
const uint8* pAlpha_selectors = layout_alpha_selectors[a][layout_index];
|
|
|
|
uint block_values[cDXT5SelectorValues];
|
|
CRNLIB_ASSERT(alpha_results.m_first_endpoint >= alpha_results.m_second_endpoint);
|
|
dxt5_block::get_block_values8(block_values, alpha_results.m_first_endpoint, alpha_results.m_second_endpoint);
|
|
|
|
for (uint cy = 0; cy < g_chunk_encodings[e].m_tiles[t].m_height; cy++) {
|
|
for (uint cx = 0; cx < g_chunk_encodings[e].m_tiles[t].m_width; cx++) {
|
|
uint s = pAlpha_selectors[cx + cy * g_chunk_encodings[e].m_tiles[t].m_width];
|
|
CRNLIB_ASSERT(s < cDXT5SelectorValues);
|
|
|
|
decomp_chunk[e](cx + g_chunk_encodings[e].m_tiles[t].m_x_ofs, cy + g_chunk_encodings[e].m_tiles[t].m_y_ofs)[m_params.m_alpha_component_indices[a]] =
|
|
static_cast<uint8>(block_values[s]);
|
|
}
|
|
}
|
|
}
|
|
} // t
|
|
|
|
if (m_params.m_hierarchical) {
|
|
if (m_has_color_blocks)
|
|
color_error_metrics[e].compute(decomp_chunk[e], orig_chunk, 0, 3);
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++)
|
|
alpha_error_metrics[a][e].compute(decomp_chunk[e], orig_chunk, m_params.m_alpha_component_indices[a], 1);
|
|
}
|
|
} // e
|
|
|
|
uint best_encoding = cNumChunkEncodings - 1;
|
|
|
|
if (m_params.m_hierarchical) {
|
|
float quality[cNumChunkEncodings];
|
|
utils::zero_object(quality);
|
|
|
|
float best_quality = 0.0f;
|
|
|
|
best_encoding = 0;
|
|
|
|
for (uint e = 0; e < cNumChunkEncodings; e++) {
|
|
if (m_has_color_blocks) {
|
|
float adaptive_tile_color_psnr_derating = m_params.m_adaptive_tile_color_psnr_derating;
|
|
if ((level_index) && (adaptive_tile_color_psnr_derating > .25f)) {
|
|
//adaptive_tile_color_psnr_derating = math::lerp(adaptive_tile_color_psnr_derating * .5f, .3f, (level_index - 1) / math::maximum(1.0f, float(m_params.m_num_levels - 2)));
|
|
adaptive_tile_color_psnr_derating = math::maximum(.25f, adaptive_tile_color_psnr_derating / powf(3.0f, static_cast<float>(level_index)));
|
|
}
|
|
|
|
float color_derating = math::lerp(0.0f, adaptive_tile_color_psnr_derating, (g_chunk_encodings[e].m_num_tiles - 1) / 3.0f);
|
|
quality[e] = (float)math::maximum<double>(color_error_metrics[e].mPeakSNR - color_derating, 0.0f);
|
|
}
|
|
|
|
if (m_num_alpha_blocks) {
|
|
quality[e] *= m_params.m_adaptive_tile_color_alpha_weighting_ratio;
|
|
float alpha_derating = math::lerp(0.0f, m_params.m_adaptive_tile_alpha_psnr_derating, (g_chunk_encodings[e].m_num_tiles - 1) / 3.0f);
|
|
|
|
float max_std_dev = 0.0f;
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
quality[e] += (float)math::maximum<double>(alpha_error_metrics[a][e].mPeakSNR - alpha_derating, 0.0f);
|
|
|
|
for (uint t = 0; t < g_chunk_encodings[e].m_num_tiles; t++) {
|
|
float std_dev = alpha_layout_std_dev[a][g_chunk_encodings[e].m_tiles[t].m_layout_index];
|
|
max_std_dev = math::maximum(max_std_dev, std_dev);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (quality[e] > best_quality) {
|
|
best_quality = quality[e];
|
|
best_encoding = e;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (uint t = 0; t < g_chunk_encodings[best_encoding].m_num_tiles; t++) {
|
|
tile_details& tile = m_tiles[chunk_index << 2 | t];
|
|
const chunk_tile_desc& layout = g_chunk_tile_layouts[g_chunk_encodings[best_encoding].m_tiles[t].m_layout_index];
|
|
for (uint y = 0; y < layout.m_height; y++) {
|
|
for (uint x = 0; x < layout.m_width; x++)
|
|
tile.pixels.push_back(m_pChunks[chunk_index](layout.m_x_ofs + x, layout.m_y_ofs + y));
|
|
}
|
|
tile.weight = (uint)(tile.pixels.size() * m_pChunks[chunk_index].m_weight);
|
|
|
|
if (m_has_color_blocks) {
|
|
tree_clusterizer<vec3F> palettizer;
|
|
for (uint p = 0; p < tile.pixels.size(); p++) {
|
|
const color_quad_u8& c = tile.pixels[p];
|
|
vec3F v(c[0] * 1.0f / 255.0f, c[1] * 1.0f / 255.0f, c[2] * 1.0f / 255.0f);
|
|
if (m_params.m_perceptual) {
|
|
v[0] *= 0.5f;
|
|
v[2] *= 0.25f;
|
|
}
|
|
palettizer.add_training_vec(v, 1);
|
|
}
|
|
palettizer.generate_codebook(2);
|
|
vec3F v[2];
|
|
utils::zero_object(v);
|
|
for (uint i = 0; i < palettizer.get_codebook_size(); i++)
|
|
v[i] = palettizer.get_codebook_entry(i);
|
|
if (palettizer.get_codebook_size() == 1)
|
|
v[1] = v[0];
|
|
if (v[0].length() > v[1].length())
|
|
utils::swap(v[0], v[1]);
|
|
vec6F vv;
|
|
for (uint i = 0; i < 2; i++) {
|
|
vv[i * 3 + 0] = v[i][0];
|
|
vv[i * 3 + 1] = v[i][1];
|
|
vv[i * 3 + 2] = v[i][2];
|
|
}
|
|
tile.color_endpoint = vv;
|
|
}
|
|
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
uint component_index = m_params.m_alpha_component_indices[a];
|
|
tree_clusterizer<vec1F> palettizer;
|
|
for (uint p = 0; p < tile.pixels.size(); p++) {
|
|
vec1F v(tile.pixels[p][component_index] * 1.0f / 255.0f);
|
|
palettizer.add_training_vec(v, 1);
|
|
}
|
|
palettizer.generate_codebook(2);
|
|
vec1F v[2];
|
|
utils::zero_object(v);
|
|
for (uint i = 0; i < palettizer.get_codebook_size(); i++)
|
|
v[i] = palettizer.get_codebook_entry(i);
|
|
if (palettizer.get_codebook_size() == 1)
|
|
v[1] = v[0];
|
|
if (v[0] > v[1])
|
|
utils::swap(v[0], v[1]);
|
|
vec2F vv(v[0][0], v[1][0]);
|
|
tile.alpha_endpoints[a] = vv;
|
|
}
|
|
}
|
|
|
|
for (uint by = 0; by < 2; by++) {
|
|
for (uint bx = 0; bx < 2; bx++) {
|
|
uint b = m_chunk_details[chunk_index].block_index[by][bx];
|
|
m_block_encodings[b] = best_encoding;
|
|
m_tile_indices[b] = chunk_index << 2 | g_tile_map[best_encoding][by][bx];
|
|
}
|
|
}
|
|
|
|
} // chunk_index
|
|
}
|
|
|
|
bool dxt_hc::determine_compressed_chunks() {
|
|
for (uint i = 0; i <= m_pTask_pool->get_num_threads(); i++)
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::determine_compressed_chunks_task, i);
|
|
m_pTask_pool->join();
|
|
|
|
m_total_tiles = 0;
|
|
for (uint t = 0; t < m_tiles.size(); t++) {
|
|
if (m_tiles[t].pixels.size())
|
|
m_total_tiles++;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void dxt_hc::determine_color_endpoint_clusters_task(uint64 data, void* pData_ptr) {
|
|
vec6F_tree_vq* vq = (vec6F_tree_vq*)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[cColorChunks] = vq->find_best_codebook_entry_fs(m_tiles[t].color_endpoint);
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::determine_color_endpoint_clusters() {
|
|
vec6F_tree_vq 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, m_tiles[t].weight);
|
|
}
|
|
|
|
vq.generate_codebook(math::minimum<uint>(m_total_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 i = 0; i < m_num_chunks; i++) {
|
|
for (uint t = m_pChunks[i].m_legacy_index << 2, tEnd = t + 4; t < tEnd; t++) {
|
|
if (m_tiles[t].pixels.size())
|
|
m_color_clusters[m_tiles[t].cluster_indices[cColorChunks]].m_pixels.append(m_tiles[t].pixels);
|
|
}
|
|
}
|
|
|
|
for (uint b = 0; b < m_blocks.size(); b++) {
|
|
uint cluster_index = m_tiles[m_tile_indices[b]].cluster_indices[cColorChunks];
|
|
m_endpoint_indices[b].component[cColorChunks] = cluster_index;
|
|
m_color_clusters[cluster_index].m_blocks[cColorChunks].push_back(b);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void dxt_hc::determine_alpha_endpoint_clusters_task(uint64 data, void* pData_ptr) {
|
|
vec2F_tree_vq* vq = (vec2F_tree_vq*)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[cAlpha0Chunks + a] = vq->find_best_codebook_entry_fs(m_tiles[t].alpha_endpoints[a]);
|
|
}
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::determine_alpha_endpoint_clusters() {
|
|
vec2F_tree_vq vq;
|
|
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_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_total_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 i = 0; i < m_num_chunks; i++) {
|
|
for (uint t = m_pChunks[i].m_legacy_index << 2, tEnd = t + 4; t < tEnd; 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[cAlpha0Chunks + a]].m_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_blocks.size(); b++) {
|
|
for (uint a = 0; a < m_num_alpha_blocks; a++) {
|
|
uint cluster_index = m_tiles[m_tile_indices[b]].cluster_indices[cAlpha0Chunks + a];
|
|
m_endpoint_indices[b].component[cAlpha0Chunks + a] = cluster_index;
|
|
m_alpha_clusters[cluster_index].m_blocks[cAlpha0Chunks + a].push_back(b);
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
endpoint_cluster& cluster = m_color_clusters[cluster_index];
|
|
if (cluster.m_pixels.empty())
|
|
continue;
|
|
|
|
crnlib::vector<uint8> selectors(cluster.m_pixels.size());
|
|
|
|
dxt1_endpoint_optimizer::params params;
|
|
params.m_block_index = cluster_index;
|
|
params.m_pPixels = cluster.m_pixels.get_ptr();
|
|
params.m_num_pixels = cluster.m_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.m_first_endpoint = results.m_low_color;
|
|
cluster.m_second_endpoint = results.m_high_color;
|
|
dxt1_block::get_block_colors4(cluster.m_color_values, cluster.m_first_endpoint, cluster.m_second_endpoint);
|
|
|
|
color_quad_u8 color_values[4];
|
|
color_values[0] = dxt1_block::unpack_color(results.m_low_color, true);
|
|
color_values[3] = dxt1_block::unpack_color(results.m_high_color, true);
|
|
for (uint c = 0; c < 3; c++) {
|
|
color_values[1].c[c] = ((color_values[0].c[c] << 1) + color_values[3].c[c] + (results.m_alternate_rounding ? 1 : 0)) / 3;
|
|
color_values[2].c[c] = ((color_values[3].c[c] << 1) + color_values[0].c[c] + (results.m_alternate_rounding ? 1 : 0)) / 3;
|
|
}
|
|
|
|
uint8 color_order[4];
|
|
for (uint8 i = 0; i < 4; i++)
|
|
color_order[i] = results.m_reordered ? 3 - g_dxt1_to_linear[i] : g_dxt1_to_linear[i];
|
|
|
|
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.m_blocks[cColorChunks];
|
|
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 = color_order[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[cColorChunks][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.m_pixels.get_ptr();
|
|
refinerParams.m_num_pixels = cluster.m_pixels.size();
|
|
refinerParams.m_dxt1_selectors = true;
|
|
refinerParams.m_error_to_beat = results.m_error;
|
|
refinerParams.m_block_index = cluster_index;
|
|
cluster.m_refined_result = refiner.refine(refinerParams, refinerResults);
|
|
if (cluster.m_refined_result) {
|
|
cluster.m_refined_first_endpoint = refinerResults.m_low_color;
|
|
cluster.m_refined_second_endpoint = refinerResults.m_high_color;
|
|
} else {
|
|
cluster.m_refined_first_endpoint = cluster.m_first_endpoint;
|
|
cluster.m_refined_second_endpoint = cluster.m_second_endpoint;
|
|
}
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::determine_color_endpoint_codebook() {
|
|
if (!m_has_color_blocks)
|
|
return true;
|
|
|
|
#if CRNLIB_ENABLE_DEBUG_MESSAGES
|
|
if (m_params.m_debugging)
|
|
console::info("Computing optimal color cluster endpoints");
|
|
#endif
|
|
|
|
for (uint i = 0; i <= m_pTask_pool->get_num_threads(); i++)
|
|
m_pTask_pool->queue_object_task(this, &dxt_hc::determine_color_endpoint_codebook_task, i, NULL);
|
|
|
|
m_pTask_pool->join();
|
|
|
|
return !m_canceled;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
endpoint_cluster& cluster = m_alpha_clusters[cluster_index];
|
|
if (cluster.m_pixels.empty())
|
|
continue;
|
|
|
|
crnlib::vector<uint8> selectors(cluster.m_pixels.size());
|
|
|
|
dxt5_endpoint_optimizer::params params;
|
|
params.m_block_index = cluster_index;
|
|
params.m_pPixels = cluster.m_pixels.get_ptr();
|
|
params.m_num_pixels = cluster.m_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.m_first_endpoint = results.m_first_endpoint;
|
|
cluster.m_second_endpoint = results.m_second_endpoint;
|
|
dxt5_block::get_block_values(cluster.m_alpha_values, cluster.m_first_endpoint, cluster.m_second_endpoint);
|
|
|
|
int delta = cluster.m_second_endpoint - cluster.m_first_endpoint;
|
|
uint8 alpha_values[8];
|
|
uint8 alpha_order[8];
|
|
for (uint sum = cluster.m_first_endpoint * 7, i = 0; i < 8; i++, sum += delta) {
|
|
alpha_values[i] = (uint8)(sum / 7);
|
|
alpha_order[i] = results.m_reordered ? 7 - g_dxt5_to_linear[i] : g_dxt5_to_linear[i];
|
|
}
|
|
|
|
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.m_blocks[cAlpha0Chunks + 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 = alpha_order[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[cAlpha0Chunks + 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.m_pixels.get_ptr();
|
|
refinerParams.m_num_pixels = cluster.m_pixels.size();
|
|
refinerParams.m_dxt1_selectors = false;
|
|
refinerParams.m_error_to_beat = results.m_error;
|
|
refinerParams.m_block_index = cluster_index;
|
|
cluster.m_refined_result = refiner.refine(refinerParams, refinerResults);
|
|
if (cluster.m_refined_result) {
|
|
cluster.m_refined_first_endpoint = refinerResults.m_low_color;
|
|
cluster.m_refined_second_endpoint = refinerResults.m_high_color;
|
|
dxt5_block::get_block_values(cluster.m_refined_alpha_values, cluster.m_refined_first_endpoint, cluster.m_refined_second_endpoint);
|
|
} else {
|
|
cluster.m_refined_first_endpoint = cluster.m_first_endpoint;
|
|
cluster.m_refined_second_endpoint = cluster.m_second_endpoint;
|
|
memcpy(cluster.m_refined_alpha_values, cluster.m_alpha_values, sizeof(cluster.m_refined_alpha_values));
|
|
}
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::determine_alpha_endpoint_codebook() {
|
|
if (!m_num_alpha_blocks)
|
|
return true;
|
|
|
|
#if CRNLIB_ENABLE_DEBUG_MESSAGES
|
|
if (m_params.m_debugging)
|
|
console::info("Computing optimal alpha cluster endpoints");
|
|
#endif
|
|
|
|
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();
|
|
|
|
return !m_canceled;
|
|
}
|
|
|
|
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 errors[16][4];
|
|
for (uint b = m_blocks.size() * data / num_tasks, bEnd = m_blocks.size() * (data + 1) / num_tasks; b < bEnd; b++) {
|
|
endpoint_cluster& cluster = m_color_clusters[m_endpoint_indices[b].color];
|
|
color_quad_u8* endpoint_colors = cluster.m_color_values;
|
|
for (uint p = 0; p < 16; p++) {
|
|
for (uint s = 0; s < 4; s++)
|
|
errors[p][s] = color::color_distance(m_params.m_perceptual, m_blocks[b][p], endpoint_colors[s], false);
|
|
}
|
|
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 = errors[0][selector & 3];
|
|
error += errors[ 1][(selector >> 2) & 3];
|
|
error += errors[ 2][(selector >> 4) & 3];
|
|
error += errors[ 3][(selector >> 6) & 3];
|
|
error += errors[ 4][(selector >> 8) & 3];
|
|
error += errors[ 5][(selector >> 10) & 3];
|
|
error += errors[ 6][(selector >> 12) & 3];
|
|
error += errors[ 7][(selector >> 14) & 3];
|
|
error += errors[ 8][(selector >> 16) & 3];
|
|
error += errors[ 9][(selector >> 18) & 3];
|
|
error += errors[10][(selector >> 20) & 3];
|
|
error += errors[11][(selector >> 22) & 3];
|
|
error += errors[12][(selector >> 24) & 3];
|
|
error += errors[13][(selector >> 26) & 3];
|
|
error += errors[14][(selector >> 28) & 3];
|
|
error += errors[15][(selector >> 30) & 3];
|
|
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] += errors[p][s];
|
|
}
|
|
selector_details[best_index].used = true;
|
|
m_selector_indices[b].color = best_index;
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::create_color_selector_codebook() {
|
|
vec16F_tree_vq selector_vq;
|
|
vec16F v;
|
|
for (uint b = 0; b < m_blocks.size(); b++) {
|
|
uint64 selector = m_block_selectors[cColorChunks][b];
|
|
for (uint8 p = 0; p < 16; p++, selector >>= 2)
|
|
v[p] = ((selector & 3) + 0.5f) * 0.25f;
|
|
selector_vq.add_training_vec(v, 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) {
|
|
uint8 s = g_dxt1_from_linear[(int)(v[j] * 4.0f)];
|
|
m_color_selectors[i] |= s << 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 best_error = errors[p][0];
|
|
uint8 best_s = 0;
|
|
for (uint8 s = 1; s < 4; s++) {
|
|
uint error = errors[p][s];
|
|
if (error < best_error) {
|
|
best_s = s;
|
|
best_error = error;
|
|
}
|
|
}
|
|
m_color_selectors[i] |= best_s << sh;
|
|
}
|
|
}
|
|
|
|
return !m_canceled;
|
|
}
|
|
|
|
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 errors[16][8];
|
|
for (uint b = m_blocks.size() * data / num_tasks, bEnd = m_blocks.size() * (data + 1) / num_tasks; b < bEnd; b++) {
|
|
for (uint c = cAlpha0Chunks; c < cAlpha0Chunks + m_num_alpha_blocks; c++) {
|
|
const uint alpha_pixel_comp = m_params.m_alpha_component_indices[c - cAlpha0Chunks];
|
|
endpoint_cluster& cluster = m_alpha_clusters[m_endpoint_indices[b].component[c]];
|
|
uint* block_values = cluster.m_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];
|
|
errors[p][s] = delta * delta;
|
|
}
|
|
}
|
|
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 = errors[0][selector & 7];
|
|
error += errors[ 1][(selector >> 3) & 7];
|
|
error += errors[ 2][(selector >> 6) & 7];
|
|
error += errors[ 3][(selector >> 9) & 7];
|
|
error += errors[ 4][(selector >> 12) & 7];
|
|
error += errors[ 5][(selector >> 15) & 7];
|
|
error += errors[ 6][(selector >> 18) & 7];
|
|
error += errors[ 7][(selector >> 21) & 7];
|
|
error += errors[ 8][(selector >> 24) & 7];
|
|
error += errors[ 9][(selector >> 27) & 7];
|
|
error += errors[10][(selector >> 30) & 7];
|
|
error += errors[11][(selector >> 33) & 7];
|
|
error += errors[12][(selector >> 36) & 7];
|
|
error += errors[13][(selector >> 39) & 7];
|
|
error += errors[14][(selector >> 42) & 7];
|
|
error += errors[15][(selector >> 45) & 7];
|
|
if (error < best_error) {
|
|
best_error = error;
|
|
best_index = s;
|
|
}
|
|
}
|
|
if (cluster.m_refined_result) {
|
|
block_values = cluster.m_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];
|
|
errors[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] += errors[p][s];
|
|
}
|
|
selector_details[best_index].used = true;
|
|
m_selector_indices[b].component[c] = best_index;
|
|
}
|
|
}
|
|
}
|
|
|
|
bool dxt_hc::create_alpha_selector_codebook() {
|
|
vec16F_tree_vq selector_vq;
|
|
vec16F v;
|
|
for (uint c = cAlpha0Chunks; c < cAlpha0Chunks + m_num_alpha_blocks; c++) {
|
|
for (uint b = 0; b < m_blocks.size(); 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) {
|
|
uint8 s = g_dxt5_from_linear[(int)(v[j] * 8.0f)];
|
|
m_alpha_selectors[i] |= (uint64)s << sh;
|
|
}
|
|
}
|
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uint num_tasks = m_pTask_pool->get_num_threads() + 1;
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crnlib::vector<crnlib::vector<alpha_selector_details>> selector_details(num_tasks);
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for (uint t = 0; t < num_tasks; t++) {
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selector_details[t].resize(m_alpha_selectors.size());
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m_pTask_pool->queue_object_task(this, &dxt_hc::create_alpha_selector_codebook_task, t, &selector_details[t]);
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}
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m_pTask_pool->join();
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|
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for (uint t = 1; t < num_tasks; t++) {
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for (uint i = 0; i < m_alpha_selectors.size(); i++) {
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for (uint8 p = 0; p < 16; p++) {
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for (uint8 s = 0; s < 8; s++)
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selector_details[0][i].error[p][s] += selector_details[t][i].error[p][s];
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}
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selector_details[0][i].used = selector_details[0][i].used || selector_details[t][i].used;
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}
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}
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|
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for (uint i = 0; i < m_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 best_error = errors[p][0];
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uint8 best_s = 0;
|
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for (uint8 s = 1; s < 8; s++) {
|
|
uint error = errors[p][s];
|
|
if (error < best_error) {
|
|
best_s = s;
|
|
best_error = error;
|
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}
|
|
}
|
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m_alpha_selectors[i] |= (uint64)best_s << sh;
|
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}
|
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}
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|
|
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return !m_canceled;
|
|
}
|
|
|
|
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);
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|
|
|
if (!m_params.m_pProgress_func)
|
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return true;
|
|
|
|
#if CRNLIB_ENABLE_DEBUG_MESSAGES
|
|
if (m_params.m_debugging)
|
|
return true;
|
|
#endif
|
|
|
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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
|