Lothario
/
axmol
mirror of https://github.com/axmolengine/axmol.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
axmol/thirdparty/astc/astcenc_weight_align.cpp

654 lines
24 KiB
C++
Raw Blame History

// 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 angular-sum algorithm for weight alignment.
*
* This algorithm works as follows:
* - we compute a complex number P as (cos s*i, sin s*i) for each weight,
* where i is the input value and s is a scaling factor based on the spacing between the weights.
* - we then add together complex numbers for all the weights.
* - we then compute the length and angle of the resulting sum.
*
* This should produce the following results:
* - perfect alignment results in a vector whose length is equal to the sum of lengths of all inputs
* - even distribution results in a vector of length 0.
* - all samples identical results in perfect alignment for every scaling.
*
* For each scaling factor within a given set, we compute an alignment factor from 0 to 1. This
* should then result in some scalings standing out as having particularly good alignment factors;
* we can use this to produce a set of candidate scale/shift values for various quantization levels;
* we should then actually try them and see what happens.
*/
#include "astcenc_internal.h"
#include "astcenc_vecmathlib.h"
#include <stdio.h>
#include <cassert>
#include <cstring>
static constexpr unsigned int ANGULAR_STEPS { 40 };
// Store a reduced sin/cos table for 64 possible weight values; this causes slight quality loss
// compared to using sin() and cos() directly. Must be 2^N.
static constexpr unsigned int SINCOS_STEPS { 64 };
static_assert((ANGULAR_STEPS % ASTCENC_SIMD_WIDTH) == 0,
"ANGULAR_STEPS must be multiple of ASTCENC_SIMD_WIDTH");
static unsigned int max_angular_steps_needed_for_quant_level[13];
// The next-to-last entry is supposed to have the value 33. This because the 32-weight mode leaves a
// double-sized hole in the middle of the weight space, so we are better off matching 33 weights.
static const unsigned int quantization_steps_for_level[13] {
2, 3, 4, 5, 6, 8, 10, 12, 16, 20, 24, 33, 36
};
alignas(ASTCENC_VECALIGN) static float sin_table[SINCOS_STEPS][ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) static float cos_table[SINCOS_STEPS][ANGULAR_STEPS];
#if !defined(NDEBUG)
static bool print_once { true };
#endif
/* See header for documentation. */
void prepare_angular_tables()
{
unsigned int max_angular_steps_needed_for_quant_steps[ANGULAR_STEPS + 1];
for (unsigned int i = 0; i < ANGULAR_STEPS; i++)
{
float angle_step = (float)(i + 1);
for (unsigned int j = 0; j < SINCOS_STEPS; j++)
{
sin_table[j][i] = static_cast<float>(sinf((2.0f * astc::PI / (SINCOS_STEPS - 1.0f)) * angle_step * static_cast<float>(j)));
cos_table[j][i] = static_cast<float>(cosf((2.0f * astc::PI / (SINCOS_STEPS - 1.0f)) * angle_step * static_cast<float>(j)));
}
max_angular_steps_needed_for_quant_steps[i + 1] = astc::min(i + 1, ANGULAR_STEPS - 1);
}
for (unsigned int i = 0; i < 13; i++)
{
max_angular_steps_needed_for_quant_level[i] = max_angular_steps_needed_for_quant_steps[quantization_steps_for_level[i]];
}
}
/**
* @brief Compute the angular alignment factors and offsets.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_quant_uvalue The decimated and quantized weight values.
* @param dec_weight_quant_sig The significance of each weight.
* @param max_angular_steps The maximum number of steps to be tested.
* @param[out] offsets The output angular offsets array.
*/
static void compute_angular_offsets(
unsigned int weight_count,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
unsigned int max_angular_steps,
float* offsets
) {
promise(weight_count > 0);
promise(max_angular_steps > 0);
alignas(ASTCENC_VECALIGN) int isamplev[BLOCK_MAX_WEIGHTS] { 0 };
// Precompute isample; arrays are always allocated 64 elements long
for (unsigned int i = 0; i < weight_count; i += ASTCENC_SIMD_WIDTH)
{
// Add 2^23 and interpreting bits extracts round-to-nearest int
vfloat sample = loada(dec_weight_quant_uvalue + i) * (SINCOS_STEPS - 1.0f) + vfloat(12582912.0f);
vint isample = float_as_int(sample) & vint((SINCOS_STEPS - 1));
storea(isample, isamplev + i);
}
// Arrays are multiple of SIMD width (ANGULAR_STEPS), safe to overshoot max
vfloat mult = vfloat(1.0f / (2.0f * astc::PI));
for (unsigned int i = 0; i < max_angular_steps; i += ASTCENC_SIMD_WIDTH)
{
vfloat anglesum_x = vfloat::zero();
vfloat anglesum_y = vfloat::zero();
for (unsigned int j = 0; j < weight_count; j++)
{
int isample = isamplev[j];
vfloat sample_weightv(dec_weight_quant_sig[j]);
anglesum_x += loada(cos_table[isample] + i) * sample_weightv;
anglesum_y += loada(sin_table[isample] + i) * sample_weightv;
}
vfloat angle = atan2(anglesum_y, anglesum_x);
vfloat ofs = angle * mult;
storea(ofs, offsets + i);
}
}
/**
* @brief For a given step size compute the lowest and highest weight.
*
* Compute the lowest and highest weight that results from quantizing using the given stepsize and
* offset, and then compute the resulting error. The cut errors indicate the error that results from
* forcing samples that should have had one weight value one step up or down.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_quant_uvalue The decimated and quantized weight values.
* @param dec_weight_quant_sig The significance of each weight.
* @param max_angular_steps The maximum number of steps to be tested.
* @param max_quant_steps The maximum quantization level to be tested.
* @param offsets The angular offsets array.
* @param[out] lowest_weight Per angular step, the lowest weight.
* @param[out] weight_span Per angular step, the span between lowest and highest weight.
* @param[out] error Per angular step, the error.
* @param[out] cut_low_weight_error Per angular step, the low weight cut error.
* @param[out] cut_high_weight_error Per angular step, the high weight cut error.
*/
static void compute_lowest_and_highest_weight(
unsigned int weight_count,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
unsigned int max_angular_steps,
unsigned int max_quant_steps,
const float* offsets,
int* lowest_weight,
int* weight_span,
float* error,
float* cut_low_weight_error,
float* cut_high_weight_error
) {
promise(weight_count > 0);
promise(max_angular_steps > 0);
vfloat rcp_stepsize = vfloat::lane_id() + vfloat(1.0f);
// Arrays are ANGULAR_STEPS long, so always safe to run full vectors
for (unsigned int sp = 0; sp < max_angular_steps; sp += ASTCENC_SIMD_WIDTH)
{
vfloat minidx(128.0f);
vfloat maxidx(-128.0f);
vfloat errval = vfloat::zero();
vfloat cut_low_weight_err = vfloat::zero();
vfloat cut_high_weight_err = vfloat::zero();
vfloat offset = loada(&offsets[sp]);
for (unsigned int j = 0; j < weight_count; ++j)
{
vfloat wt = load1(&dec_weight_quant_sig[j]);
vfloat sval = load1(&dec_weight_quant_uvalue[j]) * rcp_stepsize - offset;
vfloat svalrte = round(sval);
vfloat dif = sval - svalrte;
vfloat dwt = dif * wt;
errval += dwt * dif;
// Reset tracker on min hit
vmask mask = svalrte < minidx;
minidx = select(minidx, svalrte, mask);
cut_low_weight_err = select(cut_low_weight_err, vfloat::zero(), mask);
// Accumulate on min hit
mask = svalrte == minidx;
vfloat accum = cut_low_weight_err + wt - vfloat(2.0f) * dwt;
cut_low_weight_err = select(cut_low_weight_err, accum, mask);
// Reset tracker on max hit
mask = svalrte > maxidx;
maxidx = select(maxidx, svalrte, mask);
cut_high_weight_err = select(cut_high_weight_err, vfloat::zero(), mask);
// Accumulate on max hit
mask = svalrte == maxidx;
accum = cut_high_weight_err + wt + vfloat(2.0f) * dwt;
cut_high_weight_err = select(cut_high_weight_err, accum, mask);
}
// Write out min weight and weight span; clamp span to a usable range
vint span = float_to_int(maxidx - minidx + vfloat(1));
span = min(span, vint(max_quant_steps + 3));
span = max(span, vint(2));
storea(float_to_int(minidx), &lowest_weight[sp]);
storea(span, &weight_span[sp]);
// The cut_(lowest/highest)_weight_error indicate the error that results from forcing
// samples that should have had the weight value one step (up/down).
vfloat ssize = 1.0f / rcp_stepsize;
vfloat errscale = ssize * ssize;
storea(errval * errscale, &error[sp]);
storea(cut_low_weight_err * errscale, &cut_low_weight_error[sp]);
storea(cut_high_weight_err * errscale, &cut_high_weight_error[sp]);
rcp_stepsize = rcp_stepsize + vfloat(ASTCENC_SIMD_WIDTH);
}
}
/**
* @brief The main function for the angular algorithm.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_quant_uvalue The decimated and quantized weight value.
* @param dec_weight_quant_sig The significance of each weight.
* @param max_quant_level The maximum quantization level to be tested.
* @param[out] low_value Per angular step, the lowest weight value.
* @param[out] high_value Per angular step, the highest weight value.
*/
static void compute_angular_endpoints_for_quant_levels(
unsigned int weight_count,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
unsigned int max_quant_level,
float low_value[12],
float high_value[12]
) {
unsigned int max_quant_steps = quantization_steps_for_level[max_quant_level];
alignas(ASTCENC_VECALIGN) float angular_offsets[ANGULAR_STEPS];
unsigned int max_angular_steps = max_angular_steps_needed_for_quant_level[max_quant_level];
compute_angular_offsets(weight_count, dec_weight_quant_uvalue, dec_weight_quant_sig,
max_angular_steps, angular_offsets);
alignas(ASTCENC_VECALIGN) int32_t lowest_weight[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) int32_t weight_span[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float error[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float cut_low_weight_error[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float cut_high_weight_error[ANGULAR_STEPS];
compute_lowest_and_highest_weight(weight_count, dec_weight_quant_uvalue, dec_weight_quant_sig,
max_angular_steps, max_quant_steps,
angular_offsets, lowest_weight, weight_span, error,
cut_low_weight_error, cut_high_weight_error);
// For each quantization level, find the best error terms. Use packed vectors so data-dependent
// branches can become selects. This involves some integer to float casts, but the values are
// small enough so they never round the wrong way.
vfloat4 best_results[40];
// Initialize the array to some safe defaults
promise(max_quant_steps > 0);
for (unsigned int i = 0; i < (max_quant_steps + 4); i++)
{
// Lane<0> = Best error
// Lane<1> = Best scale; -1 indicates no solution found
// Lane<2> = Cut low weight
best_results[i] = vfloat4(ERROR_CALC_DEFAULT, -1.0f, 0.0f, 0.0f);
}
promise(max_angular_steps > 0);
for (unsigned int i = 0; i < max_angular_steps; i++)
{
int idx_span = weight_span[i];
float error_cut_low = error[i] + cut_low_weight_error[i];
float error_cut_high = error[i] + cut_high_weight_error[i];
float error_cut_low_high = error[i] + cut_low_weight_error[i] + cut_high_weight_error[i];
// Check best error against record N
vfloat4 best_result = best_results[idx_span];
vfloat4 new_result = vfloat4(error[i], (float)i, 0.0f, 0.0f);
vmask4 mask1(best_result.lane<0>() > error[i]);
best_results[idx_span] = select(best_result, new_result, mask1);
// Check best error against record N-1 with either cut low or cut high
best_result = best_results[idx_span - 1];
new_result = vfloat4(error_cut_low, (float)i, 1.0f, 0.0f);
vmask4 mask2(best_result.lane<0>() > error_cut_low);
best_result = select(best_result, new_result, mask2);
new_result = vfloat4(error_cut_high, (float)i, 0.0f, 0.0f);
vmask4 mask3(best_result.lane<0>() > error_cut_high);
best_results[idx_span - 1] = select(best_result, new_result, mask3);
// Check best error against record N-2 with both cut low and high
best_result = best_results[idx_span - 2];
new_result = vfloat4(error_cut_low_high, (float)i, 1.0f, 0.0f);
vmask4 mask4(best_result.lane<0>() > error_cut_low_high);
best_results[idx_span - 2] = select(best_result, new_result, mask4);
}
for (unsigned int i = 0; i <= max_quant_level; i++)
{
unsigned int q = quantization_steps_for_level[i];
int bsi = (int)best_results[q].lane<1>();
// Did we find anything?
#if !defined(NDEBUG)
if ((bsi < 0) && print_once)
{
print_once = false;
printf("INFO: Unable to find full encoding within search error limit.\n\n");
}
#endif
bsi = astc::max(0, bsi);
float stepsize = 1.0f / (1.0f + (float)bsi);
int lwi = lowest_weight[bsi] + (int)best_results[q].lane<2>();
int hwi = lwi + q - 1;
float offset = angular_offsets[bsi] * stepsize;
low_value[i] = offset + static_cast<float>(lwi) * stepsize;
high_value[i] = offset + static_cast<float>(hwi) * stepsize;
}
}
/**
* @brief For a given step size compute the lowest and highest weight, variant for low weight count.
*
* Compute the lowest and highest weight that results from quantizing using the given stepsize and
* offset, and then compute the resulting error. The cut errors indicate the error that results from
* forcing samples that should have had one weight value one step up or down.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_quant_uvalue The decimated and quantized weight values.
* @param dec_weight_quant_sig The significance of each weight.
* @param max_angular_steps The maximum number of steps to be tested.
* @param max_quant_steps The maximum quantization level to be tested.
* @param offsets The angular offsets array.
* @param[out] lowest_weight Per angular step, the lowest weight.
* @param[out] weight_span Per angular step, the span between lowest and highest weight.
* @param[out] error Per angular step, the error.
*/
static void compute_lowest_and_highest_weight_lwc(
unsigned int weight_count,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
unsigned int max_angular_steps,
unsigned int max_quant_steps,
const float* offsets,
int* lowest_weight,
int* weight_span,
float* error
) {
promise(weight_count > 0);
promise(max_angular_steps > 0);
vfloat rcp_stepsize = vfloat::lane_id() + vfloat(1.0f);
// Arrays are ANGULAR_STEPS long, so always safe to run full vectors
for (unsigned int sp = 0; sp < max_angular_steps; sp += ASTCENC_SIMD_WIDTH)
{
vfloat minidx(128.0f);
vfloat maxidx(-128.0f);
vfloat errval = vfloat::zero();
vfloat offset = loada(&offsets[sp]);
for (unsigned int j = 0; j < weight_count; ++j)
{
vfloat wt = load1(&dec_weight_quant_sig[j]);
vfloat sval = load1(&dec_weight_quant_uvalue[j]) * rcp_stepsize - offset;
vfloat svalrte = round(sval);
vfloat dif = sval - svalrte;
vfloat dwt = dif * wt;
errval += dwt * dif;
// Reset tracker on min hit
vmask mask = svalrte < minidx;
minidx = select(minidx, svalrte, mask);
// Reset tracker on max hit
mask = svalrte > maxidx;
maxidx = select(maxidx, svalrte, mask);
}
// Write out min weight and weight span; clamp span to a usable range
vint span = float_to_int(maxidx - minidx + vfloat(1.0f));
span = min(span, vint(max_quant_steps + 3));
span = max(span, vint(2));
storea(float_to_int(minidx), &lowest_weight[sp]);
storea(span, &weight_span[sp]);
// The cut_(lowest/highest)_weight_error indicate the error that results from forcing
// samples that should have had the weight value one step (up/down).
vfloat ssize = 1.0f / rcp_stepsize;
vfloat errscale = ssize * ssize;
storea(errval * errscale, &error[sp]);
rcp_stepsize = rcp_stepsize + vfloat(ASTCENC_SIMD_WIDTH);
}
}
/**
* @brief The main function for the angular algorithm, variant for low weight count.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_quant_uvalue The decimated and quantized weight value.
* @param dec_weight_quant_sig The significance of each weight.
* @param max_quant_level The maximum quantization level to be tested.
* @param[out] low_value Per angular step, the lowest weight value.
* @param[out] high_value Per angular step, the highest weight value.
*/
static void compute_angular_endpoints_for_quant_levels_lwc(
unsigned int weight_count,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
unsigned int max_quant_level,
float low_value[12],
float high_value[12]
) {
unsigned int max_quant_steps = quantization_steps_for_level[max_quant_level];
unsigned int max_angular_steps = max_angular_steps_needed_for_quant_level[max_quant_level];
alignas(ASTCENC_VECALIGN) float angular_offsets[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) int32_t lowest_weight[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) int32_t weight_span[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float error[ANGULAR_STEPS];
compute_angular_offsets(weight_count, dec_weight_quant_uvalue, dec_weight_quant_sig,
max_angular_steps, angular_offsets);
compute_lowest_and_highest_weight_lwc(weight_count, dec_weight_quant_uvalue, dec_weight_quant_sig,
max_angular_steps, max_quant_steps,
angular_offsets, lowest_weight, weight_span, error);
// For each quantization level, find the best error terms. Use packed vectors so data-dependent
// branches can become selects. This involves some integer to float casts, but the values are
// small enough so they never round the wrong way.
float best_error[ANGULAR_STEPS];
int best_index[ANGULAR_STEPS];
// Initialize the array to some safe defaults
promise(max_quant_steps > 0);
for (unsigned int i = 0; i < (max_quant_steps + 4); i++)
{
best_error[i] = ERROR_CALC_DEFAULT;
best_index[i] = -1;
}
promise(max_angular_steps > 0);
for (unsigned int i = 0; i < max_angular_steps; i++)
{
int idx_span = weight_span[i];
// Check best error against record N
float current_best = best_error[idx_span];
if (error[i] < current_best)
{
best_error[idx_span] = error[i];
best_index[idx_span] = i;
}
}
for (unsigned int i = 0; i <= max_quant_level; i++)
{
unsigned int q = quantization_steps_for_level[i];
int bsi = best_index[q];
// Did we find anything?
#if !defined(NDEBUG)
if ((bsi < 0) && print_once)
{
print_once = false;
printf("INFO: Unable to find low weight encoding within search error limit.\n\n");
}
#endif
bsi = astc::max(0, bsi);
int lwi = lowest_weight[bsi];
int hwi = lwi + q - 1;
low_value[i] = (angular_offsets[bsi] + static_cast<float>(lwi)) / (1.0f + (float)bsi);
high_value[i] = (angular_offsets[bsi] + static_cast<float>(hwi)) / (1.0f + (float)bsi);
}
}
/* See header for documentation. */
void compute_angular_endpoints_1plane(
unsigned int tune_low_weight_limit,
bool only_always,
const block_size_descriptor& bsd,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
float low_value[WEIGHTS_MAX_BLOCK_MODES],
float high_value[WEIGHTS_MAX_BLOCK_MODES]
) {
float low_values[WEIGHTS_MAX_DECIMATION_MODES][12];
float high_values[WEIGHTS_MAX_DECIMATION_MODES][12];
unsigned int max_decimation_modes = only_always ? bsd.always_decimation_mode_count
: bsd.decimation_mode_count;
promise(max_decimation_modes > 0);
for (unsigned int i = 0; i < max_decimation_modes; i++)
{
const decimation_mode& dm = bsd.decimation_modes[i];
if (dm.maxprec_1plane < 0 || !dm.percentile_hit)
{
continue;
}
unsigned int weight_count = bsd.decimation_tables[i]->weight_count;
if (weight_count < tune_low_weight_limit)
{
compute_angular_endpoints_for_quant_levels_lwc(
weight_count,
dec_weight_quant_uvalue + i * BLOCK_MAX_WEIGHTS,
dec_weight_quant_sig + i * BLOCK_MAX_WEIGHTS,
dm.maxprec_1plane, low_values[i], high_values[i]);
}
else
{
compute_angular_endpoints_for_quant_levels(
weight_count,
dec_weight_quant_uvalue + i * BLOCK_MAX_WEIGHTS,
dec_weight_quant_sig + i * BLOCK_MAX_WEIGHTS,
dm.maxprec_1plane, low_values[i], high_values[i]);
}
}
unsigned int max_block_modes = only_always ? bsd.always_block_mode_count
: bsd.block_mode_count;
promise(max_block_modes > 0);
for (unsigned int i = 0; i < max_block_modes; ++i)
{
const block_mode& bm = bsd.block_modes[i];
if (bm.is_dual_plane || !bm.percentile_hit)
{
continue;
}
unsigned int quant_mode = bm.quant_mode;
unsigned int decim_mode = bm.decimation_mode;
low_value[i] = low_values[decim_mode][quant_mode];
high_value[i] = high_values[decim_mode][quant_mode];
}
}
/* See header for documentation. */
void compute_angular_endpoints_2planes(
unsigned int tune_low_weight_limit,
const block_size_descriptor& bsd,
const float* dec_weight_quant_uvalue,
const float* dec_weight_quant_sig,
float low_value1[WEIGHTS_MAX_BLOCK_MODES],
float high_value1[WEIGHTS_MAX_BLOCK_MODES],
float low_value2[WEIGHTS_MAX_BLOCK_MODES],
float high_value2[WEIGHTS_MAX_BLOCK_MODES]
) {
float low_values1[WEIGHTS_MAX_DECIMATION_MODES][12];
float high_values1[WEIGHTS_MAX_DECIMATION_MODES][12];
float low_values2[WEIGHTS_MAX_DECIMATION_MODES][12];
float high_values2[WEIGHTS_MAX_DECIMATION_MODES][12];
promise(bsd.decimation_mode_count > 0);
for (unsigned int i = 0; i < bsd.decimation_mode_count; i++)
{
const decimation_mode& dm = bsd.decimation_modes[i];
if (dm.maxprec_2planes < 0 || !dm.percentile_hit)
{
continue;
}
unsigned int weight_count = bsd.decimation_tables[i]->weight_count;
if (weight_count < tune_low_weight_limit)
{
compute_angular_endpoints_for_quant_levels_lwc(
weight_count,
dec_weight_quant_uvalue + i * BLOCK_MAX_WEIGHTS,
dec_weight_quant_sig + i * BLOCK_MAX_WEIGHTS,
dm.maxprec_2planes, low_values1[i], high_values1[i]);
compute_angular_endpoints_for_quant_levels_lwc(
weight_count,
dec_weight_quant_uvalue + i * BLOCK_MAX_WEIGHTS + WEIGHTS_PLANE2_OFFSET,
dec_weight_quant_sig + i * BLOCK_MAX_WEIGHTS + WEIGHTS_PLANE2_OFFSET,
dm.maxprec_2planes, low_values2[i], high_values2[i]);
}
else
{
compute_angular_endpoints_for_quant_levels(
weight_count,
dec_weight_quant_uvalue + i * BLOCK_MAX_WEIGHTS,
dec_weight_quant_sig + i * BLOCK_MAX_WEIGHTS,
dm.maxprec_2planes, low_values1[i], high_values1[i]);
compute_angular_endpoints_for_quant_levels(
weight_count,
dec_weight_quant_uvalue + i * BLOCK_MAX_WEIGHTS + WEIGHTS_PLANE2_OFFSET,
dec_weight_quant_sig + i * BLOCK_MAX_WEIGHTS + WEIGHTS_PLANE2_OFFSET,
dm.maxprec_2planes, low_values2[i], high_values2[i]);
}
}
promise(bsd.block_mode_count > 0);
for (unsigned int i = 0; i < bsd.block_mode_count; ++i)
{
const block_mode& bm = bsd.block_modes[i];
if (!bm.is_dual_plane || !bm.percentile_hit)
{
continue;
}
unsigned int quant_mode = bm.quant_mode;
unsigned int decim_mode = bm.decimation_mode;
low_value1[i] = low_values1[decim_mode][quant_mode];
high_value1[i] = high_values1[decim_mode][quant_mode];
low_value2[i] = low_values2[decim_mode][quant_mode];
high_value2[i] = high_values2[decim_mode][quant_mode];
}
}
#endif
Reference in New Issue View Git Blame Copy Permalink