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virtualx-engine/thirdparty/astcenc/astcenc_averages_and_directions.cpp

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Add ASTC compression and decompression with Arm astcenc. Co-authored-by: Gordon A Macpherson <gordon.a.macpherson@gmail.com> Co-authored-by: Rémi Verschelde <rverschelde@gmail.com>
2022-12-20 19:54:01 +01:00
// SPDX-License-Identifier: Apache-2.0
// ----------------------------------------------------------------------------
astcenc: Update to 4.4.0 > The 4.4.0 release is a minor release with image quality improvements, > a small performance boost, a few new quality-of-life features, and a > few minor fixes for uncommon build configurations. https://github.com/ARM-software/astc-encoder/releases/tag/4.4.0
2023-05-11 14:28:49 +02:00
// Copyright 2011-2023 Arm Limited
Add ASTC compression and decompression with Arm astcenc. Co-authored-by: Gordon A Macpherson <gordon.a.macpherson@gmail.com> Co-authored-by: Rémi Verschelde <rverschelde@gmail.com>
2022-12-20 19:54:01 +01:00
//
// 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.
// ----------------------------------------------------------------------------
/**
* @brief Functions for finding dominant direction of a set of colors.
*/
#if !defined(ASTCENC_DECOMPRESS_ONLY)
#include "astcenc_internal.h"
#include <cassert>
/**
* @brief Compute the average RGB color of each partition.
*
* The algorithm here uses a vectorized sequential scan and per-partition
* color accumulators, using select() to mask texel lanes in other partitions.
*
* We only accumulate sums for N-1 partitions during the scan; the value for
* the last partition can be computed given that we know the block-wide average
* already.
*
* Because of this we could reduce the loop iteration count so it "just" spans
* the max texel index needed for the N-1 partitions, which could need fewer
* iterations than the full block texel count. However, this makes the loop
* count erratic and causes more branch mispredictions so is a net loss.
*
* @param pi The partitioning to use.
* @param blk The block data to process.
* @param[out] averages The output averages. Unused partition indices will
* not be initialized, and lane<3> will be zero.
*/
static void compute_partition_averages_rgb(
const partition_info& pi,
const image_block& blk,
vfloat4 averages[BLOCK_MAX_PARTITIONS]
) {
unsigned int partition_count = pi.partition_count;
unsigned int texel_count = blk.texel_count;
promise(texel_count > 0);
// For 1 partition just use the precomputed mean
if (partition_count == 1)
{
averages[0] = blk.data_mean.swz<0, 1, 2>();
}
// For 2 partitions scan results for partition 0, compute partition 1
else if (partition_count == 2)
{
vfloatacc pp_avg_rgb[3] {};
vint lane_id = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vint texel_partition(pi.partition_of_texel + i);
vmask lane_mask = lane_id < vint(texel_count);
lane_id += vint(ASTCENC_SIMD_WIDTH);
vmask p0_mask = lane_mask & (texel_partition == vint(0));
vfloat data_r = loada(blk.data_r + i);
haccumulate(pp_avg_rgb[0], data_r, p0_mask);
vfloat data_g = loada(blk.data_g + i);
haccumulate(pp_avg_rgb[1], data_g, p0_mask);
vfloat data_b = loada(blk.data_b + i);
haccumulate(pp_avg_rgb[2], data_b, p0_mask);
}
vfloat4 block_total = blk.data_mean.swz<0, 1, 2>() * static_cast<float>(blk.texel_count);
vfloat4 p0_total = vfloat3(hadd_s(pp_avg_rgb[0]),
hadd_s(pp_avg_rgb[1]),
hadd_s(pp_avg_rgb[2]));
vfloat4 p1_total = block_total - p0_total;
averages[0] = p0_total / static_cast<float>(pi.partition_texel_count[0]);
averages[1] = p1_total / static_cast<float>(pi.partition_texel_count[1]);
}
// For 3 partitions scan results for partition 0/1, compute partition 2
else if (partition_count == 3)
{
vfloatacc pp_avg_rgb[2][3] {};
vint lane_id = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vint texel_partition(pi.partition_of_texel + i);
vmask lane_mask = lane_id < vint(texel_count);
lane_id += vint(ASTCENC_SIMD_WIDTH);
vmask p0_mask = lane_mask & (texel_partition == vint(0));
vmask p1_mask = lane_mask & (texel_partition == vint(1));
vfloat data_r = loada(blk.data_r + i);
haccumulate(pp_avg_rgb[0][0], data_r, p0_mask);
haccumulate(pp_avg_rgb[1][0], data_r, p1_mask);
vfloat data_g = loada(blk.data_g + i);
haccumulate(pp_avg_rgb[0][1], data_g, p0_mask);
haccumulate(pp_avg_rgb[1][1], data_g, p1_mask);
vfloat data_b = loada(blk.data_b + i);
haccumulate(pp_avg_rgb[0][2], data_b, p0_mask);
haccumulate(pp_avg_rgb[1][2], data_b, p1_mask);
}
vfloat4 block_total = blk.data_mean.swz<0, 1, 2>() * static_cast<float>(blk.texel_count);
vfloat4 p0_total = vfloat3(hadd_s(pp_avg_rgb[0][0]),
hadd_s(pp_avg_rgb[0][1]),
hadd_s(pp_avg_rgb[0][2]));
vfloat4 p1_total = vfloat3(hadd_s(pp_avg_rgb[1][0]),
hadd_s(pp_avg_rgb[1][1]),
hadd_s(pp_avg_rgb[1][2]));
vfloat4 p2_total = block_total - p0_total - p1_total;
averages[0] = p0_total / static_cast<float>(pi.partition_texel_count[0]);
averages[1] = p1_total / static_cast<float>(pi.partition_texel_count[1]);
averages[2] = p2_total / static_cast<float>(pi.partition_texel_count[2]);
}
else
{
// For 4 partitions scan results for partition 0/1/2, compute partition 3
vfloatacc pp_avg_rgb[3][3] {};
vint lane_id = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vint texel_partition(pi.partition_of_texel + i);
vmask lane_mask = lane_id < vint(texel_count);
lane_id += vint(ASTCENC_SIMD_WIDTH);
vmask p0_mask = lane_mask & (texel_partition == vint(0));
vmask p1_mask = lane_mask & (texel_partition == vint(1));
vmask p2_mask = lane_mask & (texel_partition == vint(2));
vfloat data_r = loada(blk.data_r + i);
haccumulate(pp_avg_rgb[0][0], data_r, p0_mask);
haccumulate(pp_avg_rgb[1][0], data_r, p1_mask);
haccumulate(pp_avg_rgb[2][0], data_r, p2_mask);
vfloat data_g = loada(blk.data_g + i);
haccumulate(pp_avg_rgb[0][1], data_g, p0_mask);
haccumulate(pp_avg_rgb[1][1], data_g, p1_mask);
haccumulate(pp_avg_rgb[2][1], data_g, p2_mask);
vfloat data_b = loada(blk.data_b + i);
haccumulate(pp_avg_rgb[0][2], data_b, p0_mask);
haccumulate(pp_avg_rgb[1][2], data_b, p1_mask);
haccumulate(pp_avg_rgb[2][2], data_b, p2_mask);
}
vfloat4 block_total = blk.data_mean.swz<0, 1, 2>() * static_cast<float>(blk.texel_count);
vfloat4 p0_total = vfloat3(hadd_s(pp_avg_rgb[0][0]),
hadd_s(pp_avg_rgb[0][1]),
hadd_s(pp_avg_rgb[0][2]));
vfloat4 p1_total = vfloat3(hadd_s(pp_avg_rgb[1][0]),
hadd_s(pp_avg_rgb[1][1]),
hadd_s(pp_avg_rgb[1][2]));
vfloat4 p2_total = vfloat3(hadd_s(pp_avg_rgb[2][0]),
hadd_s(pp_avg_rgb[2][1]),
hadd_s(pp_avg_rgb[2][2]));
vfloat4 p3_total = block_total - p0_total - p1_total- p2_total;
averages[0] = p0_total / static_cast<float>(pi.partition_texel_count[0]);
averages[1] = p1_total / static_cast<float>(pi.partition_texel_count[1]);
averages[2] = p2_total / static_cast<float>(pi.partition_texel_count[2]);
averages[3] = p3_total / static_cast<float>(pi.partition_texel_count[3]);
}
}
/**
* @brief Compute the average RGBA color of each partition.
*
* The algorithm here uses a vectorized sequential scan and per-partition
* color accumulators, using select() to mask texel lanes in other partitions.
*
* We only accumulate sums for N-1 partitions during the scan; the value for
* the last partition can be computed given that we know the block-wide average
* already.
*
* Because of this we could reduce the loop iteration count so it "just" spans
* the max texel index needed for the N-1 partitions, which could need fewer
* iterations than the full block texel count. However, this makes the loop
* count erratic and causes more branch mispredictions so is a net loss.
*
* @param pi The partitioning to use.
* @param blk The block data to process.
* @param[out] averages The output averages. Unused partition indices will
* not be initialized.
*/
static void compute_partition_averages_rgba(
const partition_info& pi,
const image_block& blk,
vfloat4 averages[BLOCK_MAX_PARTITIONS]
) {
unsigned int partition_count = pi.partition_count;
unsigned int texel_count = blk.texel_count;
promise(texel_count > 0);
// For 1 partition just use the precomputed mean
if (partition_count == 1)
{
averages[0] = blk.data_mean;
}
// For 2 partitions scan results for partition 0, compute partition 1
else if (partition_count == 2)
{
vfloat4 pp_avg_rgba[4] {};
vint lane_id = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vint texel_partition(pi.partition_of_texel + i);
vmask lane_mask = lane_id < vint(texel_count);
lane_id += vint(ASTCENC_SIMD_WIDTH);
vmask p0_mask = lane_mask & (texel_partition == vint(0));
vfloat data_r = loada(blk.data_r + i);
haccumulate(pp_avg_rgba[0], data_r, p0_mask);
vfloat data_g = loada(blk.data_g + i);
haccumulate(pp_avg_rgba[1], data_g, p0_mask);
vfloat data_b = loada(blk.data_b + i);
haccumulate(pp_avg_rgba[2], data_b, p0_mask);
vfloat data_a = loada(blk.data_a + i);
haccumulate(pp_avg_rgba[3], data_a, p0_mask);
}
vfloat4 block_total = blk.data_mean * static_cast<float>(blk.texel_count);
vfloat4 p0_total = vfloat4(hadd_s(pp_avg_rgba[0]),
hadd_s(pp_avg_rgba[1]),
hadd_s(pp_avg_rgba[2]),
hadd_s(pp_avg_rgba[3]));
vfloat4 p1_total = block_total - p0_total;
averages[0] = p0_total / static_cast<float>(pi.partition_texel_count[0]);
averages[1] = p1_total / static_cast<float>(pi.partition_texel_count[1]);
}
// For 3 partitions scan results for partition 0/1, compute partition 2
else if (partition_count == 3)
{
vfloat4 pp_avg_rgba[2][4] {};
vint lane_id = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vint texel_partition(pi.partition_of_texel + i);
vmask lane_mask = lane_id < vint(texel_count);
lane_id += vint(ASTCENC_SIMD_WIDTH);
vmask p0_mask = lane_mask & (texel_partition == vint(0));
vmask p1_mask = lane_mask & (texel_partition == vint(1));
vfloat data_r = loada(blk.data_r + i);
haccumulate(pp_avg_rgba[0][0], data_r, p0_mask);
haccumulate(pp_avg_rgba[1][0], data_r, p1_mask);
vfloat data_g = loada(blk.data_g + i);
haccumulate(pp_avg_rgba[0][1], data_g, p0_mask);
haccumulate(pp_avg_rgba[1][1], data_g, p1_mask);
vfloat data_b = loada(blk.data_b + i);
haccumulate(pp_avg_rgba[0][2], data_b, p0_mask);
haccumulate(pp_avg_rgba[1][2], data_b, p1_mask);
vfloat data_a = loada(blk.data_a + i);
haccumulate(pp_avg_rgba[0][3], data_a, p0_mask);
haccumulate(pp_avg_rgba[1][3], data_a, p1_mask);
}
vfloat4 block_total = blk.data_mean * static_cast<float>(blk.texel_count);
vfloat4 p0_total = vfloat4(hadd_s(pp_avg_rgba[0][0]),
hadd_s(pp_avg_rgba[0][1]),
hadd_s(pp_avg_rgba[0][2]),
hadd_s(pp_avg_rgba[0][3]));
vfloat4 p1_total = vfloat4(hadd_s(pp_avg_rgba[1][0]),
hadd_s(pp_avg_rgba[1][1]),
hadd_s(pp_avg_rgba[1][2]),
hadd_s(pp_avg_rgba[1][3]));
vfloat4 p2_total = block_total - p0_total - p1_total;
averages[0] = p0_total / static_cast<float>(pi.partition_texel_count[0]);
averages[1] = p1_total / static_cast<float>(pi.partition_texel_count[1]);
averages[2] = p2_total / static_cast<float>(pi.partition_texel_count[2]);
}
else
{
// For 4 partitions scan results for partition 0/1/2, compute partition 3
vfloat4 pp_avg_rgba[3][4] {};
vint lane_id = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vint texel_partition(pi.partition_of_texel + i);
vmask lane_mask = lane_id < vint(texel_count);
lane_id += vint(ASTCENC_SIMD_WIDTH);
vmask p0_mask = lane_mask & (texel_partition == vint(0));
vmask p1_mask = lane_mask & (texel_partition == vint(1));
vmask p2_mask = lane_mask & (texel_partition == vint(2));
vfloat data_r = loada(blk.data_r + i);
haccumulate(pp_avg_rgba[0][0], data_r, p0_mask);
haccumulate(pp_avg_rgba[1][0], data_r, p1_mask);
haccumulate(pp_avg_rgba[2][0], data_r, p2_mask);
vfloat data_g = loada(blk.data_g + i);
haccumulate(pp_avg_rgba[0][1], data_g, p0_mask);
haccumulate(pp_avg_rgba[1][1], data_g, p1_mask);
haccumulate(pp_avg_rgba[2][1], data_g, p2_mask);
vfloat data_b = loada(blk.data_b + i);
haccumulate(pp_avg_rgba[0][2], data_b, p0_mask);
haccumulate(pp_avg_rgba[1][2], data_b, p1_mask);
haccumulate(pp_avg_rgba[2][2], data_b, p2_mask);
vfloat data_a = loada(blk.data_a + i);
haccumulate(pp_avg_rgba[0][3], data_a, p0_mask);
haccumulate(pp_avg_rgba[1][3], data_a, p1_mask);
haccumulate(pp_avg_rgba[2][3], data_a, p2_mask);
}
vfloat4 block_total = blk.data_mean * static_cast<float>(blk.texel_count);
vfloat4 p0_total = vfloat4(hadd_s(pp_avg_rgba[0][0]),
hadd_s(pp_avg_rgba[0][1]),
hadd_s(pp_avg_rgba[0][2]),
hadd_s(pp_avg_rgba[0][3]));
vfloat4 p1_total = vfloat4(hadd_s(pp_avg_rgba[1][0]),
hadd_s(pp_avg_rgba[1][1]),
hadd_s(pp_avg_rgba[1][2]),
hadd_s(pp_avg_rgba[1][3]));
vfloat4 p2_total = vfloat4(hadd_s(pp_avg_rgba[2][0]),
hadd_s(pp_avg_rgba[2][1]),
hadd_s(pp_avg_rgba[2][2]),
hadd_s(pp_avg_rgba[2][3]));
vfloat4 p3_total = block_total - p0_total - p1_total- p2_total;
averages[0] = p0_total / static_cast<float>(pi.partition_texel_count[0]);
averages[1] = p1_total / static_cast<float>(pi.partition_texel_count[1]);
averages[2] = p2_total / static_cast<float>(pi.partition_texel_count[2]);
averages[3] = p3_total / static_cast<float>(pi.partition_texel_count[3]);
}
}
/* See header for documentation. */
void compute_avgs_and_dirs_4_comp(
const partition_info& pi,
const image_block& blk,
partition_metrics pm[BLOCK_MAX_PARTITIONS]
) {
int partition_count = pi.partition_count;
promise(partition_count > 0);
// Pre-compute partition_averages
vfloat4 partition_averages[BLOCK_MAX_PARTITIONS];
compute_partition_averages_rgba(pi, blk, partition_averages);
for (int partition = 0; partition < partition_count; partition++)
{
const uint8_t *texel_indexes = pi.texels_of_partition[partition];
unsigned int texel_count = pi.partition_texel_count[partition];
promise(texel_count > 0);
vfloat4 average = partition_averages[partition];
pm[partition].avg = average;
vfloat4 sum_xp = vfloat4::zero();
vfloat4 sum_yp = vfloat4::zero();
vfloat4 sum_zp = vfloat4::zero();
vfloat4 sum_wp = vfloat4::zero();
for (unsigned int i = 0; i < texel_count; i++)
{
unsigned int iwt = texel_indexes[i];
vfloat4 texel_datum = blk.texel(iwt);
texel_datum = texel_datum - average;
vfloat4 zero = vfloat4::zero();
vmask4 tdm0 = texel_datum.swz<0,0,0,0>() > zero;
sum_xp += select(zero, texel_datum, tdm0);
vmask4 tdm1 = texel_datum.swz<1,1,1,1>() > zero;
sum_yp += select(zero, texel_datum, tdm1);
vmask4 tdm2 = texel_datum.swz<2,2,2,2>() > zero;
sum_zp += select(zero, texel_datum, tdm2);
vmask4 tdm3 = texel_datum.swz<3,3,3,3>() > zero;
sum_wp += select(zero, texel_datum, tdm3);
}
vfloat4 prod_xp = dot(sum_xp, sum_xp);
vfloat4 prod_yp = dot(sum_yp, sum_yp);
vfloat4 prod_zp = dot(sum_zp, sum_zp);
vfloat4 prod_wp = dot(sum_wp, sum_wp);
vfloat4 best_vector = sum_xp;
vfloat4 best_sum = prod_xp;
vmask4 mask = prod_yp > best_sum;
best_vector = select(best_vector, sum_yp, mask);
best_sum = select(best_sum, prod_yp, mask);
mask = prod_zp > best_sum;
best_vector = select(best_vector, sum_zp, mask);
best_sum = select(best_sum, prod_zp, mask);
mask = prod_wp > best_sum;
best_vector = select(best_vector, sum_wp, mask);
pm[partition].dir = best_vector;
}
}
/* See header for documentation. */
void compute_avgs_and_dirs_3_comp(
const partition_info& pi,
const image_block& blk,
unsigned int omitted_component,
partition_metrics pm[BLOCK_MAX_PARTITIONS]
) {
// Pre-compute partition_averages
vfloat4 partition_averages[BLOCK_MAX_PARTITIONS];
compute_partition_averages_rgba(pi, blk, partition_averages);
const float* data_vr = blk.data_r;
const float* data_vg = blk.data_g;
const float* data_vb = blk.data_b;
// TODO: Data-driven permute would be useful to avoid this ...
if (omitted_component == 0)
{
partition_averages[0] = partition_averages[0].swz<1, 2, 3>();
partition_averages[1] = partition_averages[1].swz<1, 2, 3>();
partition_averages[2] = partition_averages[2].swz<1, 2, 3>();
partition_averages[3] = partition_averages[3].swz<1, 2, 3>();
data_vr = blk.data_g;
data_vg = blk.data_b;
data_vb = blk.data_a;
}
else if (omitted_component == 1)
{
partition_averages[0] = partition_averages[0].swz<0, 2, 3>();
partition_averages[1] = partition_averages[1].swz<0, 2, 3>();
partition_averages[2] = partition_averages[2].swz<0, 2, 3>();
partition_averages[3] = partition_averages[3].swz<0, 2, 3>();
data_vg = blk.data_b;
data_vb = blk.data_a;
}
else if (omitted_component == 2)
{
partition_averages[0] = partition_averages[0].swz<0, 1, 3>();
partition_averages[1] = partition_averages[1].swz<0, 1, 3>();
partition_averages[2] = partition_averages[2].swz<0, 1, 3>();
partition_averages[3] = partition_averages[3].swz<0, 1, 3>();
data_vb = blk.data_a;
}
else
{
partition_averages[0] = partition_averages[0].swz<0, 1, 2>();
partition_averages[1] = partition_averages[1].swz<0, 1, 2>();
partition_averages[2] = partition_averages[2].swz<0, 1, 2>();
partition_averages[3] = partition_averages[3].swz<0, 1, 2>();
}
unsigned int partition_count = pi.partition_count;
promise(partition_count > 0);
for (unsigned int partition = 0; partition < partition_count; partition++)
{
const uint8_t *texel_indexes = pi.texels_of_partition[partition];
unsigned int texel_count = pi.partition_texel_count[partition];
promise(texel_count > 0);
vfloat4 average = partition_averages[partition];
pm[partition].avg = average;
vfloat4 sum_xp = vfloat4::zero();
vfloat4 sum_yp = vfloat4::zero();
vfloat4 sum_zp = vfloat4::zero();
for (unsigned int i = 0; i < texel_count; i++)
{
unsigned int iwt = texel_indexes[i];
vfloat4 texel_datum = vfloat3(data_vr[iwt],
data_vg[iwt],
data_vb[iwt]);
texel_datum = texel_datum - average;
vfloat4 zero = vfloat4::zero();
vmask4 tdm0 = texel_datum.swz<0,0,0,0>() > zero;
sum_xp += select(zero, texel_datum, tdm0);
vmask4 tdm1 = texel_datum.swz<1,1,1,1>() > zero;
sum_yp += select(zero, texel_datum, tdm1);
vmask4 tdm2 = texel_datum.swz<2,2,2,2>() > zero;
sum_zp += select(zero, texel_datum, tdm2);
}
vfloat4 prod_xp = dot(sum_xp, sum_xp);
vfloat4 prod_yp = dot(sum_yp, sum_yp);
vfloat4 prod_zp = dot(sum_zp, sum_zp);
vfloat4 best_vector = sum_xp;
vfloat4 best_sum = prod_xp;
vmask4 mask = prod_yp > best_sum;
best_vector = select(best_vector, sum_yp, mask);
best_sum = select(best_sum, prod_yp, mask);
mask = prod_zp > best_sum;
best_vector = select(best_vector, sum_zp, mask);
pm[partition].dir = best_vector;
}
}
/* See header for documentation. */
void compute_avgs_and_dirs_3_comp_rgb(
const partition_info& pi,
const image_block& blk,
partition_metrics pm[BLOCK_MAX_PARTITIONS]
) {
unsigned int partition_count = pi.partition_count;
promise(partition_count > 0);
// Pre-compute partition_averages
vfloat4 partition_averages[BLOCK_MAX_PARTITIONS];
compute_partition_averages_rgb(pi, blk, partition_averages);
for (unsigned int partition = 0; partition < partition_count; partition++)
{
const uint8_t *texel_indexes = pi.texels_of_partition[partition];
unsigned int texel_count = pi.partition_texel_count[partition];
promise(texel_count > 0);
vfloat4 average = partition_averages[partition];
pm[partition].avg = average;
vfloat4 sum_xp = vfloat4::zero();
vfloat4 sum_yp = vfloat4::zero();
vfloat4 sum_zp = vfloat4::zero();
for (unsigned int i = 0; i < texel_count; i++)
{
unsigned int iwt = texel_indexes[i];
vfloat4 texel_datum = blk.texel3(iwt);
texel_datum = texel_datum - average;
vfloat4 zero = vfloat4::zero();
vmask4 tdm0 = texel_datum.swz<0,0,0,0>() > zero;
sum_xp += select(zero, texel_datum, tdm0);
vmask4 tdm1 = texel_datum.swz<1,1,1,1>() > zero;
sum_yp += select(zero, texel_datum, tdm1);
vmask4 tdm2 = texel_datum.swz<2,2,2,2>() > zero;
sum_zp += select(zero, texel_datum, tdm2);
}
vfloat4 prod_xp = dot(sum_xp, sum_xp);
vfloat4 prod_yp = dot(sum_yp, sum_yp);
vfloat4 prod_zp = dot(sum_zp, sum_zp);
vfloat4 best_vector = sum_xp;
vfloat4 best_sum = prod_xp;
vmask4 mask = prod_yp > best_sum;
best_vector = select(best_vector, sum_yp, mask);
best_sum = select(best_sum, prod_yp, mask);
mask = prod_zp > best_sum;
best_vector = select(best_vector, sum_zp, mask);
pm[partition].dir = best_vector;
}
}
/* See header for documentation. */
void compute_avgs_and_dirs_2_comp(
const partition_info& pt,
const image_block& blk,
unsigned int component1,
unsigned int component2,
partition_metrics pm[BLOCK_MAX_PARTITIONS]
) {
vfloat4 average;
const float* data_vr = nullptr;
const float* data_vg = nullptr;
if (component1 == 0 && component2 == 1)
{
average = blk.data_mean.swz<0, 1>();
data_vr = blk.data_r;
data_vg = blk.data_g;
}
else if (component1 == 0 && component2 == 2)
{
average = blk.data_mean.swz<0, 2>();
data_vr = blk.data_r;
data_vg = blk.data_b;
}
else // (component1 == 1 && component2 == 2)
{
assert(component1 == 1 && component2 == 2);
average = blk.data_mean.swz<1, 2>();
data_vr = blk.data_g;
data_vg = blk.data_b;
}
unsigned int partition_count = pt.partition_count;
promise(partition_count > 0);
for (unsigned int partition = 0; partition < partition_count; partition++)
{
const uint8_t *texel_indexes = pt.texels_of_partition[partition];
unsigned int texel_count = pt.partition_texel_count[partition];
promise(texel_count > 0);
// Only compute a partition mean if more than one partition
if (partition_count > 1)
{
average = vfloat4::zero();
for (unsigned int i = 0; i < texel_count; i++)
{
unsigned int iwt = texel_indexes[i];
average += vfloat2(data_vr[iwt], data_vg[iwt]);
}
average = average / static_cast<float>(texel_count);
}
pm[partition].avg = average;
vfloat4 sum_xp = vfloat4::zero();
vfloat4 sum_yp = vfloat4::zero();
for (unsigned int i = 0; i < texel_count; i++)
{
unsigned int iwt = texel_indexes[i];
vfloat4 texel_datum = vfloat2(data_vr[iwt], data_vg[iwt]);
texel_datum = texel_datum - average;
vfloat4 zero = vfloat4::zero();
vmask4 tdm0 = texel_datum.swz<0,0,0,0>() > zero;
sum_xp += select(zero, texel_datum, tdm0);
vmask4 tdm1 = texel_datum.swz<1,1,1,1>() > zero;
sum_yp += select(zero, texel_datum, tdm1);
}
vfloat4 prod_xp = dot(sum_xp, sum_xp);
vfloat4 prod_yp = dot(sum_yp, sum_yp);
vfloat4 best_vector = sum_xp;
vfloat4 best_sum = prod_xp;
vmask4 mask = prod_yp > best_sum;
best_vector = select(best_vector, sum_yp, mask);
pm[partition].dir = best_vector;
}
}
/* See header for documentation. */
void compute_error_squared_rgba(
const partition_info& pi,
const image_block& blk,
const processed_line4 uncor_plines[BLOCK_MAX_PARTITIONS],
const processed_line4 samec_plines[BLOCK_MAX_PARTITIONS],
astcenc: Update to 4.4.0 > The 4.4.0 release is a minor release with image quality improvements, > a small performance boost, a few new quality-of-life features, and a > few minor fixes for uncommon build configurations. https://github.com/ARM-software/astc-encoder/releases/tag/4.4.0
2023-05-11 14:28:49 +02:00
float line_lengths[BLOCK_MAX_PARTITIONS],
Add ASTC compression and decompression with Arm astcenc. Co-authored-by: Gordon A Macpherson <gordon.a.macpherson@gmail.com> Co-authored-by: Rémi Verschelde <rverschelde@gmail.com>
2022-12-20 19:54:01 +01:00
float& uncor_error,
float& samec_error
) {
unsigned int partition_count = pi.partition_count;
promise(partition_count > 0);
vfloatacc uncor_errorsumv = vfloatacc::zero();
vfloatacc samec_errorsumv = vfloatacc::zero();
for (unsigned int partition = 0; partition < partition_count; partition++)
{
const uint8_t *texel_indexes = pi.texels_of_partition[partition];
processed_line4 l_uncor = uncor_plines[partition];
processed_line4 l_samec = samec_plines[partition];
unsigned int texel_count = pi.partition_texel_count[partition];
promise(texel_count > 0);
// Vectorize some useful scalar inputs
vfloat l_uncor_bs0(l_uncor.bs.lane<0>());
vfloat l_uncor_bs1(l_uncor.bs.lane<1>());
vfloat l_uncor_bs2(l_uncor.bs.lane<2>());
vfloat l_uncor_bs3(l_uncor.bs.lane<3>());
vfloat l_uncor_amod0(l_uncor.amod.lane<0>());
vfloat l_uncor_amod1(l_uncor.amod.lane<1>());
vfloat l_uncor_amod2(l_uncor.amod.lane<2>());
vfloat l_uncor_amod3(l_uncor.amod.lane<3>());
vfloat l_samec_bs0(l_samec.bs.lane<0>());
vfloat l_samec_bs1(l_samec.bs.lane<1>());
vfloat l_samec_bs2(l_samec.bs.lane<2>());
vfloat l_samec_bs3(l_samec.bs.lane<3>());
assert(all(l_samec.amod == vfloat4(0.0f)));
vfloat uncor_loparamv(1e10f);
vfloat uncor_hiparamv(-1e10f);
vfloat ew_r(blk.channel_weight.lane<0>());
vfloat ew_g(blk.channel_weight.lane<1>());
vfloat ew_b(blk.channel_weight.lane<2>());
vfloat ew_a(blk.channel_weight.lane<3>());
// This implementation over-shoots, but this is safe as we initialize the texel_indexes
// array to extend the last value. This means min/max are not impacted, but we need to mask
// out the dummy values when we compute the line weighting.
vint lane_ids = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vmask mask = lane_ids < vint(texel_count);
vint texel_idxs(texel_indexes + i);
vfloat data_r = gatherf(blk.data_r, texel_idxs);
vfloat data_g = gatherf(blk.data_g, texel_idxs);
vfloat data_b = gatherf(blk.data_b, texel_idxs);
vfloat data_a = gatherf(blk.data_a, texel_idxs);
vfloat uncor_param = (data_r * l_uncor_bs0)
+ (data_g * l_uncor_bs1)
+ (data_b * l_uncor_bs2)
+ (data_a * l_uncor_bs3);
uncor_loparamv = min(uncor_param, uncor_loparamv);
uncor_hiparamv = max(uncor_param, uncor_hiparamv);
vfloat uncor_dist0 = (l_uncor_amod0 - data_r)
+ (uncor_param * l_uncor_bs0);
vfloat uncor_dist1 = (l_uncor_amod1 - data_g)
+ (uncor_param * l_uncor_bs1);
vfloat uncor_dist2 = (l_uncor_amod2 - data_b)
+ (uncor_param * l_uncor_bs2);
vfloat uncor_dist3 = (l_uncor_amod3 - data_a)
+ (uncor_param * l_uncor_bs3);
vfloat uncor_err = (ew_r * uncor_dist0 * uncor_dist0)
+ (ew_g * uncor_dist1 * uncor_dist1)
+ (ew_b * uncor_dist2 * uncor_dist2)
+ (ew_a * uncor_dist3 * uncor_dist3);
haccumulate(uncor_errorsumv, uncor_err, mask);
// Process samechroma data
vfloat samec_param = (data_r * l_samec_bs0)
+ (data_g * l_samec_bs1)
+ (data_b * l_samec_bs2)
+ (data_a * l_samec_bs3);
vfloat samec_dist0 = samec_param * l_samec_bs0 - data_r;
vfloat samec_dist1 = samec_param * l_samec_bs1 - data_g;
vfloat samec_dist2 = samec_param * l_samec_bs2 - data_b;
vfloat samec_dist3 = samec_param * l_samec_bs3 - data_a;
vfloat samec_err = (ew_r * samec_dist0 * samec_dist0)
+ (ew_g * samec_dist1 * samec_dist1)
+ (ew_b * samec_dist2 * samec_dist2)
+ (ew_a * samec_dist3 * samec_dist3);
haccumulate(samec_errorsumv, samec_err, mask);
lane_ids += vint(ASTCENC_SIMD_WIDTH);
}
// Turn very small numbers and NaNs into a small number
astcenc: Update to 4.4.0 > The 4.4.0 release is a minor release with image quality improvements, > a small performance boost, a few new quality-of-life features, and a > few minor fixes for uncommon build configurations. https://github.com/ARM-software/astc-encoder/releases/tag/4.4.0
2023-05-11 14:28:49 +02:00
float uncor_linelen = hmax_s(uncor_hiparamv) - hmin_s(uncor_loparamv);
line_lengths[partition] = astc::max(uncor_linelen, 1e-7f);
Add ASTC compression and decompression with Arm astcenc. Co-authored-by: Gordon A Macpherson <gordon.a.macpherson@gmail.com> Co-authored-by: Rémi Verschelde <rverschelde@gmail.com>
2022-12-20 19:54:01 +01:00
}
uncor_error = hadd_s(uncor_errorsumv);
samec_error = hadd_s(samec_errorsumv);
}
/* See header for documentation. */
void compute_error_squared_rgb(
const partition_info& pi,
const image_block& blk,
partition_lines3 plines[BLOCK_MAX_PARTITIONS],
float& uncor_error,
float& samec_error
) {
unsigned int partition_count = pi.partition_count;
promise(partition_count > 0);
vfloatacc uncor_errorsumv = vfloatacc::zero();
vfloatacc samec_errorsumv = vfloatacc::zero();
for (unsigned int partition = 0; partition < partition_count; partition++)
{
partition_lines3& pl = plines[partition];
const uint8_t *texel_indexes = pi.texels_of_partition[partition];
unsigned int texel_count = pi.partition_texel_count[partition];
promise(texel_count > 0);
processed_line3 l_uncor = pl.uncor_pline;
processed_line3 l_samec = pl.samec_pline;
// Vectorize some useful scalar inputs
vfloat l_uncor_bs0(l_uncor.bs.lane<0>());
vfloat l_uncor_bs1(l_uncor.bs.lane<1>());
vfloat l_uncor_bs2(l_uncor.bs.lane<2>());
vfloat l_uncor_amod0(l_uncor.amod.lane<0>());
vfloat l_uncor_amod1(l_uncor.amod.lane<1>());
vfloat l_uncor_amod2(l_uncor.amod.lane<2>());
vfloat l_samec_bs0(l_samec.bs.lane<0>());
vfloat l_samec_bs1(l_samec.bs.lane<1>());
vfloat l_samec_bs2(l_samec.bs.lane<2>());
assert(all(l_samec.amod == vfloat4(0.0f)));
vfloat uncor_loparamv(1e10f);
vfloat uncor_hiparamv(-1e10f);
vfloat ew_r(blk.channel_weight.lane<0>());
vfloat ew_g(blk.channel_weight.lane<1>());
vfloat ew_b(blk.channel_weight.lane<2>());
// This implementation over-shoots, but this is safe as we initialize the weights array
// to extend the last value. This means min/max are not impacted, but we need to mask
// out the dummy values when we compute the line weighting.
vint lane_ids = vint::lane_id();
for (unsigned int i = 0; i < texel_count; i += ASTCENC_SIMD_WIDTH)
{
vmask mask = lane_ids < vint(texel_count);
vint texel_idxs(texel_indexes + i);
vfloat data_r = gatherf(blk.data_r, texel_idxs);
vfloat data_g = gatherf(blk.data_g, texel_idxs);
vfloat data_b = gatherf(blk.data_b, texel_idxs);
vfloat uncor_param = (data_r * l_uncor_bs0)
+ (data_g * l_uncor_bs1)
+ (data_b * l_uncor_bs2);
uncor_loparamv = min(uncor_param, uncor_loparamv);
uncor_hiparamv = max(uncor_param, uncor_hiparamv);
vfloat uncor_dist0 = (l_uncor_amod0 - data_r)
+ (uncor_param * l_uncor_bs0);
vfloat uncor_dist1 = (l_uncor_amod1 - data_g)
+ (uncor_param * l_uncor_bs1);
vfloat uncor_dist2 = (l_uncor_amod2 - data_b)
+ (uncor_param * l_uncor_bs2);
vfloat uncor_err = (ew_r * uncor_dist0 * uncor_dist0)
+ (ew_g * uncor_dist1 * uncor_dist1)
+ (ew_b * uncor_dist2 * uncor_dist2);
haccumulate(uncor_errorsumv, uncor_err, mask);
// Process samechroma data
vfloat samec_param = (data_r * l_samec_bs0)
+ (data_g * l_samec_bs1)
+ (data_b * l_samec_bs2);
vfloat samec_dist0 = samec_param * l_samec_bs0 - data_r;
vfloat samec_dist1 = samec_param * l_samec_bs1 - data_g;
vfloat samec_dist2 = samec_param * l_samec_bs2 - data_b;
vfloat samec_err = (ew_r * samec_dist0 * samec_dist0)
+ (ew_g * samec_dist1 * samec_dist1)
+ (ew_b * samec_dist2 * samec_dist2);
haccumulate(samec_errorsumv, samec_err, mask);
lane_ids += vint(ASTCENC_SIMD_WIDTH);
}
// Turn very small numbers and NaNs into a small number
astcenc: Update to 4.4.0 > The 4.4.0 release is a minor release with image quality improvements, > a small performance boost, a few new quality-of-life features, and a > few minor fixes for uncommon build configurations. https://github.com/ARM-software/astc-encoder/releases/tag/4.4.0
2023-05-11 14:28:49 +02:00
float uncor_linelen = hmax_s(uncor_hiparamv) - hmin_s(uncor_loparamv);
pl.line_length = astc::max(uncor_linelen, 1e-7f);
Add ASTC compression and decompression with Arm astcenc. Co-authored-by: Gordon A Macpherson <gordon.a.macpherson@gmail.com> Co-authored-by: Rémi Verschelde <rverschelde@gmail.com>
2022-12-20 19:54:01 +01:00
}
uncor_error = hadd_s(uncor_errorsumv);
samec_error = hadd_s(samec_errorsumv);
}
#endif
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