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// SPDX-License-Identifier: Apache-2.0
// ----------------------------------------------------------------------------
// Copyright 2011-2021 Arm Limited
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not
// use this file except in compliance with the License. You may obtain a copy
// of the License at:
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
// License for the specific language governing permissions and limitations
// under the License.
// ----------------------------------------------------------------------------
#if !defined(ASTCENC_DECOMPRESS_ONLY)
/**
* @brief Functions for finding best partition for a block.
*
* The partition search operates in two stages. The first pass uses kmeans clustering to group
* texels into an ideal partitioning for the requested partition count, and then compares that
* against the 1024 partitionings generated by the ASTC partition hash function. The generated
* partitions are then ranked by the number of texels in the wrong partition, compared to the ideal
* clustering. All 1024 partitions are tested for similarity and ranked, apart from duplicates and
* partitionings that actually generate fewer than the requested partition count, but only the top
* N candidates are actually put through a more detailed search. N is determined by the compressor
* quality preset.
*
* For the detailed search, each candidate is checked against two possible encoding methods:
*
* - The best partitioning assuming different chroma colors (RGB + RGB or RGB + delta endpoints).
* - The best partitioning assuming same chroma colors (RGB + scale endpoints).
*
* This is implemented by computing the compute mean color and dominant direction for each
* partition. This defines two lines, both of which go through the mean color value.
*
* - One line has a direction defined by the dominant direction; this is used to assess the error
* from using an uncorrelated color representation.
* - The other line goes through (0,0,0,1) and is used to assess the error from using a same chroma
* (RGB + scale) color representation.
*
* The best candidate is selected by computing the squared-errors that result from using these
* lines for endpoint selection.
*/
#include "astcenc_internal.h"
/**
* @brief Pick some initital kmeans cluster centers.
*
* @param blk The image block color data to compress.
* @param texel_count The number of texels in the block.
* @param partition_count The number of partitions in the block.
* @param[out] cluster_centers The initital partition cluster center colors.
*/
static void kmeans_init(
const image_block& blk,
unsigned int texel_count,
unsigned int partition_count,
vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS]
) {
promise(texel_count > 0);
promise(partition_count > 0);
unsigned int clusters_selected = 0;
float distances[BLOCK_MAX_TEXELS];
// Pick a random sample as first cluster center; 145897 from random.org
unsigned int sample = 145897 % texel_count;
vfloat4 center_color = blk.texel(sample);
cluster_centers[clusters_selected] = center_color;
clusters_selected++;
// Compute the distance to the first cluster center
float distance_sum = 0.0f;
for (unsigned int i = 0; i < texel_count; i++)
{
vfloat4 color = blk.texel(i);
vfloat4 diff = color - center_color;
float distance = dot_s(diff, diff);
distance_sum += distance;
distances[i] = distance;
}
// More numbers from random.org for weighted-random center selection
const float cluster_cutoffs[9] = {
0.626220f, 0.932770f, 0.275454f,
0.318558f, 0.240113f, 0.009190f,
0.347661f, 0.731960f, 0.156391f
};
unsigned int cutoff = (clusters_selected - 1) + 3 * (partition_count - 2);
// Pick the remaining samples as needed
while (true)
{
// Pick the next center in a weighted-random fashion.
float summa = 0.0f;
float distance_cutoff = distance_sum * cluster_cutoffs[cutoff++];
for (sample = 0; sample < texel_count; sample++)
{
summa += distances[sample];
if (summa >= distance_cutoff)
{
break;
}
}
// Clamp to a valid range and store the selected cluster center
sample = astc::min(sample, texel_count - 1);
center_color = blk.texel(sample);
cluster_centers[clusters_selected++] = center_color;
if (clusters_selected >= partition_count)
{
break;
}
// Compute the distance to the new cluster center, keep the min dist
distance_sum = 0.0f;
for (unsigned int i = 0; i < texel_count; i++)
{
vfloat4 color = blk.texel(i);
vfloat4 diff = color - center_color;
float distance = dot_s(diff, diff);
distance = astc::min(distance, distances[i]);
distance_sum += distance;
distances[i] = distance;
}
}
}
/**
* @brief Assign texels to clusters, based on a set of chosen center points.
*
* @param blk The image block color data to compress.
* @param texel_count The number of texels in the block.
* @param partition_count The number of partitions in the block.
* @param cluster_centers The partition cluster center colors.
* @param[out] partition_of_texel The partition assigned for each texel.
*/
static void kmeans_assign(
const image_block& blk,
unsigned int texel_count,
unsigned int partition_count,
const vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS],
uint8_t partition_of_texel[BLOCK_MAX_TEXELS]
) {
promise(texel_count > 0);
promise(partition_count > 0);
uint8_t partition_texel_count[BLOCK_MAX_PARTITIONS] { 0 };
// Find the best partition for every texel
for (unsigned int i = 0; i < texel_count; i++)
{
float best_distance = std::numeric_limits<float>::max();
unsigned int best_partition = 0;
vfloat4 color = blk.texel(i);
for (unsigned int j = 0; j < partition_count; j++)
{
vfloat4 diff = color - cluster_centers[j];
float distance = dot_s(diff, diff);
if (distance < best_distance)
{
best_distance = distance;
best_partition = j;
}
}
partition_of_texel[i] = best_partition;
partition_texel_count[best_partition]++;
}
// It is possible to get a situation where a partition ends up without any texels. In this case,
// assign texel N to partition N. This is silly, but ensures that every partition retains at
// least one texel. Reassigning a texel in this manner may cause another partition to go empty,
// so if we actually did a reassignment, run the whole loop over again.
bool problem_case;
do
{
problem_case = false;
for (unsigned int i = 0; i < partition_count; i++)
{
if (partition_texel_count[i] == 0)
{
partition_texel_count[partition_of_texel[i]]--;
partition_texel_count[i]++;
partition_of_texel[i] = i;
problem_case = true;
}
}
} while (problem_case);
}
/**
* @brief Compute new cluster centers based on their center of gravity.
*
* @param blk The image block color data to compress.
* @param texel_count The number of texels in the block.
* @param partition_count The number of partitions in the block.
* @param[out] cluster_centers The new cluster center colors.
* @param partition_of_texel The partition assigned for each texel.
*/
static void kmeans_update(
const image_block& blk,
unsigned int texel_count,
unsigned int partition_count,
vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS],
const uint8_t partition_of_texel[BLOCK_MAX_TEXELS]
) {
promise(texel_count > 0);
promise(partition_count > 0);
vfloat4 color_sum[BLOCK_MAX_PARTITIONS] {
vfloat4::zero(),
vfloat4::zero(),
vfloat4::zero(),
vfloat4::zero()
};
uint8_t partition_texel_count[BLOCK_MAX_PARTITIONS] { 0 };
// Find the center-of-gravity in each cluster
for (unsigned int i = 0; i < texel_count; i++)
{
uint8_t partition = partition_of_texel[i];
color_sum[partition] += blk.texel(i);;
partition_texel_count[partition]++;
}
// Set the center of gravity to be the new cluster center
for (unsigned int i = 0; i < partition_count; i++)
{
float scale = 1.0f / static_cast<float>(partition_texel_count[i]);
cluster_centers[i] = color_sum[i] * scale;
}
}
/**
* @brief Compute bit-mismatch for partitioning in 2-partition mode.
*
* @param a The texel assignment bitvector for the block.
* @param b The texel assignment bitvector for the partition table.
*
* @return The number of bit mismatches.
*/
static inline unsigned int partition_mismatch2(
const uint64_t a[2],
const uint64_t b[2]
) {
int v1 = astc::popcount(a[0] ^ b[0]) + astc::popcount(a[1] ^ b[1]);
int v2 = astc::popcount(a[0] ^ b[1]) + astc::popcount(a[1] ^ b[0]);
return astc::min(v1, v2);
}
/**
* @brief Compute bit-mismatch for partitioning in 3-partition mode.
*
* @param a The texel assignment bitvector for the block.
* @param b The texel assignment bitvector for the partition table.
*
* @return The number of bit mismatches.
*/
static inline unsigned int partition_mismatch3(
const uint64_t a[3],
const uint64_t b[3]
) {
int p00 = astc::popcount(a[0] ^ b[0]);
int p01 = astc::popcount(a[0] ^ b[1]);
int p02 = astc::popcount(a[0] ^ b[2]);
int p10 = astc::popcount(a[1] ^ b[0]);
int p11 = astc::popcount(a[1] ^ b[1]);
int p12 = astc::popcount(a[1] ^ b[2]);
int p20 = astc::popcount(a[2] ^ b[0]);
int p21 = astc::popcount(a[2] ^ b[1]);
int p22 = astc::popcount(a[2] ^ b[2]);
int s0 = p11 + p22;
int s1 = p12 + p21;
int v0 = astc::min(s0, s1) + p00;
int s2 = p10 + p22;
int s3 = p12 + p20;
int v1 = astc::min(s2, s3) + p01;
int s4 = p10 + p21;
int s5 = p11 + p20;
int v2 = astc::min(s4, s5) + p02;
return astc::min(v0, v1, v2);
}
/**
* @brief Compute bit-mismatch for partitioning in 4-partition mode.
*
* @param a The texel assignment bitvector for the block.
* @param b The texel assignment bitvector for the partition table.
*
* @return The number of bit mismatches.
*/
static inline unsigned int partition_mismatch4(
const uint64_t a[4],
const uint64_t b[4]
) {
int p00 = astc::popcount(a[0] ^ b[0]);
int p01 = astc::popcount(a[0] ^ b[1]);
int p02 = astc::popcount(a[0] ^ b[2]);
int p03 = astc::popcount(a[0] ^ b[3]);
int p10 = astc::popcount(a[1] ^ b[0]);
int p11 = astc::popcount(a[1] ^ b[1]);
int p12 = astc::popcount(a[1] ^ b[2]);
int p13 = astc::popcount(a[1] ^ b[3]);
int p20 = astc::popcount(a[2] ^ b[0]);
int p21 = astc::popcount(a[2] ^ b[1]);
int p22 = astc::popcount(a[2] ^ b[2]);
int p23 = astc::popcount(a[2] ^ b[3]);
int p30 = astc::popcount(a[3] ^ b[0]);
int p31 = astc::popcount(a[3] ^ b[1]);
int p32 = astc::popcount(a[3] ^ b[2]);
int p33 = astc::popcount(a[3] ^ b[3]);
int mx23 = astc::min(p22 + p33, p23 + p32);
int mx13 = astc::min(p21 + p33, p23 + p31);
int mx12 = astc::min(p21 + p32, p22 + p31);
int mx03 = astc::min(p20 + p33, p23 + p30);
int mx02 = astc::min(p20 + p32, p22 + p30);
int mx01 = astc::min(p21 + p30, p20 + p31);
int v0 = p00 + astc::min(p11 + mx23, p12 + mx13, p13 + mx12);
int v1 = p01 + astc::min(p10 + mx23, p12 + mx03, p13 + mx02);
int v2 = p02 + astc::min(p11 + mx03, p10 + mx13, p13 + mx01);
int v3 = p03 + astc::min(p11 + mx02, p12 + mx01, p10 + mx12);
return astc::min(v0, v1, v2, v3);
}
using mismatch_dispatch = unsigned int (*)(const uint64_t*, const uint64_t*);
/**
* @brief Count the partition table mismatches vs the data clustering.
*
* @param bsd The block size information.
* @param partition_count The number of partitions in the block.
* @param bitmaps The block texel partition assignment patterns.
* @param[out] mismatch_counts The array storing per partitioning mismatch counts.
*/
static void count_partition_mismatch_bits(
const block_size_descriptor& bsd,
unsigned int partition_count,
const uint64_t bitmaps[BLOCK_MAX_PARTITIONS],
unsigned int mismatch_counts[BLOCK_MAX_PARTITIONINGS]
) {
const auto* pt = bsd.get_partition_table(partition_count);
// Function pointer dispatch table
const mismatch_dispatch dispatch[3] {
partition_mismatch2,
partition_mismatch3,
partition_mismatch4
};
for (unsigned int i = 0; i < BLOCK_MAX_PARTITIONINGS; i++)
{
int bitcount = 255;
if (pt->partition_count == partition_count)
{
bitcount = dispatch[partition_count - 2](bitmaps, pt->coverage_bitmaps);
}
mismatch_counts[i] = bitcount;
pt++;
}
}
/**
* @brief Use counting sort on the mismatch array to sort partition candidates.
*
* @param mismatch_count Partitioning mismatch counts, in index order.
* @param[out] partition_ordering Partition index values, in mismatch order.
*/
static void get_partition_ordering_by_mismatch_bits(
const unsigned int mismatch_count[BLOCK_MAX_PARTITIONINGS],
unsigned int partition_ordering[BLOCK_MAX_PARTITIONINGS]
) {
unsigned int mscount[256] { 0 };
// Create the histogram of mismatch counts
for (unsigned int i = 0; i < BLOCK_MAX_PARTITIONINGS; i++)
{
mscount[mismatch_count[i]]++;
}
// Create a running sum from the histogram array
// Cells store previous values only; i.e. exclude self after sum
unsigned int summa = 0;
for (unsigned int i = 0; i < 256; i++)
{
unsigned int cnt = mscount[i];
mscount[i] = summa;
summa += cnt;
}
// Use the running sum as the index, incrementing after read to allow
// sequential entries with the same count
for (unsigned int i = 0; i < BLOCK_MAX_PARTITIONINGS; i++)
{
unsigned int idx = mscount[mismatch_count[i]]++;
partition_ordering[idx] = i;
}
}
/**
* @brief Use k-means clustering to compute a partition ordering for a block..
*
* @param bsd The block size information.
* @param blk The image block color data to compress.
* @param partition_count The desired number of partitions in the block.
* @param[out] partition_ordering The list of recommended partition indices, in priority order.
*/
static void compute_kmeans_partition_ordering(
const block_size_descriptor& bsd,
const image_block& blk,
unsigned int partition_count,
unsigned int partition_ordering[BLOCK_MAX_PARTITIONINGS]
) {
vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS];
uint8_t texel_partitions[BLOCK_MAX_TEXELS];
// Use three passes of k-means clustering to partition the block data
for (unsigned int i = 0; i < 3; i++)
{
if (i == 0)
{
kmeans_init(blk, bsd.texel_count, partition_count, cluster_centers);
}
else
{
kmeans_update(blk, bsd.texel_count, partition_count, cluster_centers, texel_partitions);
}
kmeans_assign(blk, bsd.texel_count, partition_count, cluster_centers, texel_partitions);
}
// Construct the block bitmaps of texel assignments to each partition
uint64_t bitmaps[BLOCK_MAX_PARTITIONS] { 0 };
unsigned int texels_to_process = astc::min(bsd.texel_count, BLOCK_MAX_KMEANS_TEXELS);
promise(texels_to_process > 0);
for (unsigned int i = 0; i < texels_to_process; i++)
{
unsigned int idx = bsd.kmeans_texels[i];
bitmaps[texel_partitions[idx]] |= 1ULL << i;
}
// Count the mismatch between the block and the format's partition tables
unsigned int mismatch_counts[BLOCK_MAX_PARTITIONINGS];
count_partition_mismatch_bits(bsd, partition_count, bitmaps, mismatch_counts);
// Sort the partitions based on the number of mismatched bits
get_partition_ordering_by_mismatch_bits(mismatch_counts, partition_ordering);
}
/* See header for documentation. */
void find_best_partition_candidates(
const block_size_descriptor& bsd,
const image_block& blk,
const error_weight_block& ewb,
unsigned int partition_count,
unsigned int partition_search_limit,
unsigned int& best_partition_uncor,
unsigned int& best_partition_samec,
unsigned int* best_partition_dualplane
) {
// Constant used to estimate quantization error for a given partitioning; the optimal value for
// this depends on bitrate. These values have been determined empirically.
unsigned int texels_per_block = bsd.texel_count;
float weight_imprecision_estim = 0.055f;
if (texels_per_block <= 20)
{
weight_imprecision_estim = 0.03f;
}
else if (texels_per_block <= 31)
{
weight_imprecision_estim = 0.04f;
}
else if (texels_per_block <= 41)
{
weight_imprecision_estim = 0.05f;
}
promise(partition_count > 0);
promise(partition_search_limit > 0);
weight_imprecision_estim = weight_imprecision_estim * weight_imprecision_estim;
unsigned int partition_sequence[BLOCK_MAX_PARTITIONINGS];
compute_kmeans_partition_ordering(bsd, blk, partition_count, partition_sequence);
bool uses_alpha = blk.is_using_alpha();
// Partitioning errors assuming uncorrelated-chrominance endpoints
float uncor_best_error { ERROR_CALC_DEFAULT };
unsigned int uncor_best_partition { 0 };
// Partitioning errors assuming same-chrominance endpoints
// Store two so we can always return one different to uncorr
float samec_best_errors[2] { ERROR_CALC_DEFAULT, ERROR_CALC_DEFAULT };
unsigned int samec_best_partitions[2] { 0, 0 };
// Partitioning errors assuming that one color component is uncorrelated
float sep_best_error { ERROR_CALC_DEFAULT };
unsigned int sep_best_partition { 0 };
unsigned int sep_best_component { 0 };
bool skip_two_plane = best_partition_dualplane == nullptr;
if (uses_alpha)
{
for (unsigned int i = 0; i < partition_search_limit; i++)
{
unsigned int partition = partition_sequence[i];
const auto& pi = bsd.get_partition_info(partition_count, partition);
unsigned int bk_partition_count = pi.partition_count;
if (bk_partition_count < partition_count)
{
break;
}
// Compute weighting to give to each component in each partition
partition_metrics pms[BLOCK_MAX_PARTITIONS];
compute_avgs_and_dirs_4_comp(pi, blk, ewb, pms);
line4 uncor_lines[BLOCK_MAX_PARTITIONS];
line4 samec_lines[BLOCK_MAX_PARTITIONS];
line3 sep_r_lines[BLOCK_MAX_PARTITIONS];
line3 sep_g_lines[BLOCK_MAX_PARTITIONS];
line3 sep_b_lines[BLOCK_MAX_PARTITIONS];
line3 sep_a_lines[BLOCK_MAX_PARTITIONS];
processed_line4 uncor_plines[BLOCK_MAX_PARTITIONS];
processed_line4 samec_plines[BLOCK_MAX_PARTITIONS];
float uncor_line_lens[BLOCK_MAX_PARTITIONS];
float samec_line_lens[BLOCK_MAX_PARTITIONS];
for (unsigned int j = 0; j < partition_count; j++)
{
partition_metrics& pm = pms[j];
uncor_lines[j].a = pm.avg;
uncor_lines[j].b = normalize_safe(pm.dir, unit4());
uncor_plines[j].amod = (uncor_lines[j].a - uncor_lines[j].b * dot(uncor_lines[j].a, uncor_lines[j].b)) * pm.icolor_scale;
uncor_plines[j].bs = uncor_lines[j].b * pm.color_scale;
uncor_plines[j].bis = uncor_lines[j].b * pm.icolor_scale;
samec_lines[j].a = vfloat4::zero();
samec_lines[j].b = normalize_safe(pm.avg, unit4());
samec_plines[j].amod = vfloat4::zero();
samec_plines[j].bs = samec_lines[j].b * pm.color_scale;
samec_plines[j].bis = samec_lines[j].b * pm.icolor_scale;
if (!skip_two_plane)
{
sep_r_lines[j].a = pm.avg.swz<1, 2, 3>();
vfloat4 dirs_gba = pm.dir.swz<1, 2, 3>();
sep_r_lines[j].b = normalize_safe(dirs_gba, unit3());
sep_g_lines[j].a = pm.avg.swz<0, 2, 3>();
vfloat4 dirs_rba = pm.dir.swz<0, 2, 3>();
sep_g_lines[j].b = normalize_safe(dirs_rba, unit3());
sep_b_lines[j].a = pm.avg.swz<0, 1, 3>();
vfloat4 dirs_rga = pm.dir.swz<0, 1, 3>();
sep_b_lines[j].b = normalize_safe(dirs_rga, unit3());
sep_a_lines[j].a = pm.avg.swz<0, 1, 2>();
vfloat4 dirs_rgb = pm.dir.swz<0, 1, 2>();
sep_a_lines[j].b = normalize_safe(dirs_rgb, unit3());
}
}
float uncor_error = 0.0f;
float samec_error = 0.0f;
vfloat4 sep_error = vfloat4::zero();
compute_error_squared_rgba(pi,
blk,
ewb,
uncor_plines,
samec_plines,
uncor_line_lens,
samec_line_lens,
uncor_error,
samec_error);
// Compute an estimate of error introduced by weight quantization imprecision.
// This error is computed as follows, for each partition
// 1: compute the principal-axis vector (full length) in error-space
// 2: convert the principal-axis vector to regular RGB-space
// 3: scale the vector by a constant that estimates average quantization error
// 4: for each texel, square the vector, then do a dot-product with the texel's
// error weight; sum up the results across all texels.
// 4(optimized): square the vector once, then do a dot-product with the average
// texel error, then multiply by the number of texels.
for (unsigned int j = 0; j < partition_count; j++)
{
partition_metrics& pm = pms[j];
float tpp = (float)(pi.partition_texel_count[j]);
vfloat4 ics = pm.icolor_scale;
vfloat4 error_weights = pm.error_weight * (tpp * weight_imprecision_estim);
vfloat4 uncor_vector = uncor_lines[j].b * uncor_line_lens[j] * ics;
vfloat4 samec_vector = samec_lines[j].b * samec_line_lens[j] * ics;
uncor_vector = uncor_vector * uncor_vector;
samec_vector = samec_vector * samec_vector;
uncor_error += dot_s(uncor_vector, error_weights);
samec_error += dot_s(samec_vector, error_weights);
if (!skip_two_plane)
{
vfloat4 sep_r_vector = sep_r_lines[j].b * ics.swz<1, 2, 3, 0>();
vfloat4 sep_g_vector = sep_g_lines[j].b * ics.swz<0, 2, 3, 1>();
vfloat4 sep_b_vector = sep_b_lines[j].b * ics.swz<0, 1, 3, 2>();
vfloat4 sep_a_vector = sep_a_lines[j].b * ics.swz<0, 1, 2, 3>();
sep_r_vector = sep_r_vector * sep_r_vector;
sep_g_vector = sep_g_vector * sep_g_vector;
sep_b_vector = sep_b_vector * sep_b_vector;
sep_a_vector = sep_a_vector * sep_a_vector;
vfloat4 sep_err_inc(dot3_s(sep_r_vector, error_weights.swz<1, 2, 3, 0>()),
dot3_s(sep_g_vector, error_weights.swz<0, 2, 3, 1>()),
dot3_s(sep_b_vector, error_weights.swz<0, 1, 3, 2>()),
dot3_s(sep_a_vector, error_weights.swz<0, 1, 2, 3>()));
sep_error = sep_error + sep_err_inc + pm.range_sq * error_weights;
}
}
if (uncor_error < uncor_best_error)
{
uncor_best_error = uncor_error;
uncor_best_partition = partition;
}
if (samec_error < samec_best_errors[0])
{
samec_best_errors[1] = samec_best_errors[0];
samec_best_partitions[1] = samec_best_partitions[0];
samec_best_errors[0] = samec_error;
samec_best_partitions[0] = partition;
}
else if (samec_error < samec_best_errors[1])
{
samec_best_errors[1] = samec_error;
samec_best_partitions[1] = partition;
}
if (!skip_two_plane)
{
if (sep_error.lane<0>() < sep_best_error)
{
sep_best_error = sep_error.lane<0>();
sep_best_partition = partition;
sep_best_component = 0;
}
if (sep_error.lane<1>() < sep_best_error)
{
sep_best_error = sep_error.lane<1>();
sep_best_partition = partition;
sep_best_component = 1;
}
if (sep_error.lane<2>() < sep_best_error)
{
sep_best_error = sep_error.lane<2>();
sep_best_partition = partition;
sep_best_component = 2;
}
if (sep_error.lane<3>() < sep_best_error)
{
sep_best_error = sep_error.lane<3>();
sep_best_partition = partition;
sep_best_component = 3;
}
}
}
}
else
{
for (unsigned int i = 0; i < partition_search_limit; i++)
{
unsigned int partition = partition_sequence[i];
const auto& pi = bsd.get_partition_info(partition_count, partition);
unsigned int bk_partition_count = pi.partition_count;
if (bk_partition_count < partition_count)
{
break;
}
// Compute weighting to give to each component in each partition
partition_metrics pms[BLOCK_MAX_PARTITIONS];
compute_avgs_and_dirs_3_comp(pi, blk, ewb, 3, pms);
partition_lines3 plines[BLOCK_MAX_PARTITIONS];
line2 sep_r_lines[BLOCK_MAX_PARTITIONS];
line2 sep_g_lines[BLOCK_MAX_PARTITIONS];
line2 sep_b_lines[BLOCK_MAX_PARTITIONS];
for (unsigned int j = 0; j < partition_count; j++)
{
partition_metrics& pm = pms[j];
partition_lines3& pl = plines[j];
pl.uncor_line.a = pm.avg;
pl.uncor_line.b = normalize_safe(pm.dir.swz<0, 1, 2>(), unit3());
pl.samec_line.a = vfloat4::zero();
pl.samec_line.b = normalize_safe(pm.avg.swz<0, 1, 2>(), unit3());
pl.uncor_pline.amod = (pl.uncor_line.a - pl.uncor_line.b * dot3(pl.uncor_line.a, pl.uncor_line.b)) * pm.icolor_scale.swz<0, 1, 2, 3>();
pl.uncor_pline.bs = (pl.uncor_line.b * pm.color_scale.swz<0, 1, 2, 3>());
pl.uncor_pline.bis = (pl.uncor_line.b * pm.icolor_scale.swz<0, 1, 2, 3>());
pl.samec_pline.amod = vfloat4::zero();
pl.samec_pline.bs = (pl.samec_line.b * pm.color_scale.swz<0, 1, 2, 3>());
pl.samec_pline.bis = (pl.samec_line.b * pm.icolor_scale.swz<0, 1, 2, 3>());
if (!skip_two_plane)
{
sep_r_lines[j].a = pm.avg.swz<1, 2>();
vfloat4 dirs_gb = pm.dir.swz<1, 2>();
sep_r_lines[j].b = normalize_safe(dirs_gb, unit2());
sep_g_lines[j].a = pm.avg.swz<0, 2>();
vfloat4 dirs_rb = pm.dir.swz<0, 2>();
sep_g_lines[j].b = normalize_safe(dirs_rb, unit2());
sep_b_lines[j].a = pm.avg.swz<0, 1>();
vfloat4 dirs_rg = pm.dir.swz<0, 1>();
sep_b_lines[j].b = normalize_safe(dirs_rg, unit2());
}
}
float uncor_error = 0.0f;
float samec_error = 0.0f;
vfloat4 sep_error = vfloat4::zero();
compute_error_squared_rgb(pi,
blk,
ewb,
plines,
uncor_error,
samec_error);
// Compute an estimate of error introduced by weight quantization imprecision.
// This error is computed as follows, for each partition
// 1: compute the principal-axis vector (full length) in error-space
// 2: convert the principal-axis vector to regular RGB-space
// 3: scale the vector by a constant that estimates average quantization error
// 4: for each texel, square the vector, then do a dot-product with the texel's
// error weight; sum up the results across all texels.
// 4(optimized): square the vector once, then do a dot-product with the average
// texel error, then multiply by the number of texels.
for (unsigned int j = 0; j < partition_count; j++)
{
partition_metrics& pm = pms[j];
partition_lines3& pl = plines[j];
float tpp = (float)(pi.partition_texel_count[j]);
vfloat4 ics = pm.icolor_scale;
ics.set_lane<3>(0.0f);
vfloat4 error_weights = pm.error_weight * (tpp * weight_imprecision_estim);
error_weights.set_lane<3>(0.0f);
vfloat4 uncor_vector = (pl.uncor_line.b * pl.uncor_line_len) * ics;
vfloat4 samec_vector = (pl.samec_line.b * pl.samec_line_len) * ics;
uncor_vector = uncor_vector * uncor_vector;
samec_vector = samec_vector * samec_vector;
uncor_error += dot3_s(uncor_vector, error_weights);
samec_error += dot3_s(samec_vector, error_weights);
if (!skip_two_plane)
{
vfloat4 sep_r_vector = sep_r_lines[j].b * ics.swz<1, 2>();
vfloat4 sep_g_vector = sep_g_lines[j].b * ics.swz<0, 2>();
vfloat4 sep_b_vector = sep_b_lines[j].b * ics.swz<0, 1>();
sep_r_vector = sep_r_vector * sep_r_vector;
sep_g_vector = sep_g_vector * sep_g_vector;
sep_b_vector = sep_b_vector * sep_b_vector;
sep_error.set_lane<0>(sep_error.lane<0>() + dot_s(sep_r_vector, error_weights.swz<1, 2>()));
sep_error.set_lane<1>(sep_error.lane<1>() + dot_s(sep_g_vector, error_weights.swz<0, 2>()));
sep_error.set_lane<2>(sep_error.lane<2>() + dot_s(sep_b_vector, error_weights.swz<0, 1>()));
sep_error.set_lane<0>(sep_error.lane<0>() + pm.range_sq.lane<0>() * error_weights.lane<0>());
sep_error.set_lane<1>(sep_error.lane<1>() + pm.range_sq.lane<1>() * error_weights.lane<1>());
sep_error.set_lane<2>(sep_error.lane<2>() + pm.range_sq.lane<2>() * error_weights.lane<2>());
}
}
if (uncor_error < uncor_best_error)
{
uncor_best_error = uncor_error;
uncor_best_partition = partition;
}
if (samec_error < samec_best_errors[0])
{
samec_best_errors[1] = samec_best_errors[0];
samec_best_partitions[1] = samec_best_partitions[0];
samec_best_errors[0] = samec_error;
samec_best_partitions[0] = partition;
}
else if (samec_error < samec_best_errors[1])
{
samec_best_errors[1] = samec_error;
samec_best_partitions[1] = partition;
}
if (!skip_two_plane)
{
if (sep_error.lane<0>() < sep_best_error)
{
sep_best_error = sep_error.lane<0>();
sep_best_partition = partition;
sep_best_component = 0;
}
if (sep_error.lane<1>() < sep_best_error)
{
sep_best_error = sep_error.lane<1>();
sep_best_partition = partition;
sep_best_component = 1;
}
if (sep_error.lane<2>() < sep_best_error)
{
sep_best_error = sep_error.lane<2>();
sep_best_partition = partition;
sep_best_component = 2;
}
}
}
}
best_partition_uncor = uncor_best_partition;
unsigned int index = samec_best_partitions[0] != uncor_best_partition ? 0 : 1;
best_partition_samec = samec_best_partitions[index];
if (best_partition_dualplane)
{
*best_partition_dualplane = (sep_best_component << PARTITION_INDEX_BITS) |
(sep_best_partition);
}
}
#endif
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