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Convert predictor_enc.c to fixed point

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Also remove the last float in histogram_enc.c

Change-Id: I6f647a5fc6dd34a19292820817472b4462c94f49
This commit is contained in:
Vincent Rabaud 2024-08-29 10:09:39 +02:00
parent 94de6c7fed
commit 2e81017c7a
2 changed files with 51 additions and 45 deletions
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5
src/enc/histogram_enc.c
View File

@ -1247,9 +1247,10 @@ int VP8LGetHistoImageSymbols(int xsize, int ysize,
// Don't combine the histograms using stochastic and greedy heuristics for
// low-effort compression mode.
if (!low_effort || !entropy_combine) {
const float x = quality / 100.f;
// cubic ramp between 1 and MAX_HISTO_GREEDY:
const int threshold_size = (int)(1 + (x * x * x) * (MAX_HISTO_GREEDY - 1));
const int threshold_size =
(int)(1 + DivRound(quality * quality * quality * (MAX_HISTO_GREEDY - 1),
100 * 100 * 100));
int do_greedy;
if (!HistogramCombineStochastic(image_histo, &num_used, threshold_size,
&do_greedy)) {

91
src/enc/predictor_enc.c
View File

@ -21,9 +21,8 @@
#include "src/enc/vp8i_enc.h"
#include "src/enc/vp8li_enc.h"
#define MAX_DIFF_COST (1e30f)
#define HISTO_SIZE (4 * 256)
static const float kSpatialPredictorBias = 15.f;
static const int64_t kSpatialPredictorBias = 15ll << LOG_2_PRECISION_BITS;
static const int kPredLowEffort = 11;
static const uint32_t kMaskAlpha = 0xff000000;
@ -35,33 +34,34 @@ static WEBP_INLINE int GetMin(int a, int b) { return (a > b) ? b : a; }
// Compute a bias for prediction entropy using a global heuristic to favor
// values closer to 0. Hence the final negative sign.
static float PredictionCostBias(const uint32_t counts[256], int weight_0,
float exp_val) {
// 'exp_val' has a scaling factor of 1/100.
static int64_t PredictionCostBias(const uint32_t counts[256], uint64_t weight_0,
uint64_t exp_val) {
const int significant_symbols = 256 >> 4;
const float exp_decay_factor = 0.6f;
float bits = (float)weight_0 * counts[0];
const uint64_t exp_decay_factor = 6; // has a scaling factor of 1/10
uint64_t bits = (weight_0 * counts[0]) << LOG_2_PRECISION_BITS;
int i;
exp_val <<= LOG_2_PRECISION_BITS;
for (i = 1; i < significant_symbols; ++i) {
bits += exp_val * (counts[i] + counts[256 - i]);
exp_val *= exp_decay_factor;
bits += DivRound(exp_val * (counts[i] + counts[256 - i]), 100);
exp_val = DivRound(exp_decay_factor * exp_val, 10);
}
return (float)(-0.1 * bits);
return -DivRound((int64_t)bits, 10);
}
static float PredictionCostSpatialHistogram(
static int64_t PredictionCostSpatialHistogram(
const uint32_t accumulated[HISTO_SIZE], const uint32_t tile[HISTO_SIZE],
int mode, int left_mode, int above_mode) {
int i;
float retval = 0.f;
int64_t retval = 0;
for (i = 0; i < 4; ++i) {
const float kExpValue = 0.94f;
const uint64_t kExpValue = 94;
retval += PredictionCostBias(&tile[i * 256], 1, kExpValue);
// Compute the new cost if 'tile' is added to 'accumulate' but also add the
// cost of the current histogram to guide the spatial predictor selection.
// Basically, favor low entropy, locally and globally.
retval += (float)VP8LCombinedShannonEntropy(&tile[i * 256],
&accumulated[i * 256]) /
(1ll << LOG_2_PRECISION_BITS);
retval += (int64_t)VP8LCombinedShannonEntropy(&tile[i * 256],
&accumulated[i * 256]);
}
// Favor keeping the areas locally similar.
if (mode == left_mode) retval -= kSpatialPredictorBias;
@ -341,7 +341,7 @@ static int GetBestPredictorForTile(
uint32_t* upper_row = argb_scratch;
uint32_t* current_row = upper_row + width + 1;
uint8_t* const max_diffs = (uint8_t*)(current_row + width + 1);
float best_diff = MAX_DIFF_COST;
int64_t best_diff = WEBP_INT64_MAX;
int best_mode = 0;
int mode;
uint32_t histo_stack_1[HISTO_SIZE];
@ -354,7 +354,7 @@ static int GetBestPredictorForTile(
assert(max_x <= (1 << MAX_TRANSFORM_BITS));
for (mode = 0; mode < kNumPredModes; ++mode) {
float cur_diff;
int64_t cur_diff;
int relative_y;
memset(histo_argb, 0, sizeof(histo_stack_1));
if (start_y > 0) {
@ -607,35 +607,36 @@ static WEBP_INLINE uint32_t MultipliersToColorCode(
m->green_to_red_;
}
static float PredictionCostCrossColor(const uint32_t accumulated[256],
const uint32_t counts[256]) {
static int64_t PredictionCostCrossColor(const uint32_t accumulated[256],
const uint32_t counts[256]) {
// Favor low entropy, locally and globally.
// Favor small absolute values for PredictionCostSpatial
static const float kExpValue = 2.4f;
return (float)VP8LCombinedShannonEntropy(counts, accumulated) /
(1ll << LOG_2_PRECISION_BITS) +
static const uint64_t kExpValue = 240;
return (int64_t)VP8LCombinedShannonEntropy(counts, accumulated) +
PredictionCostBias(counts, 3, kExpValue);
}
static float GetPredictionCostCrossColorRed(
static int64_t GetPredictionCostCrossColorRed(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y, int green_to_red,
const uint32_t accumulated_red_histo[256]) {
uint32_t histo[256] = { 0 };
float cur_diff;
int64_t cur_diff;
VP8LCollectColorRedTransforms(argb, stride, tile_width, tile_height,
green_to_red, histo);
cur_diff = PredictionCostCrossColor(accumulated_red_histo, histo);
if ((uint8_t)green_to_red == prev_x.green_to_red_) {
cur_diff -= 3; // favor keeping the areas locally similar
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)green_to_red == prev_y.green_to_red_) {
cur_diff -= 3; // favor keeping the areas locally similar
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if (green_to_red == 0) {
cur_diff -= 3;
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
return cur_diff;
}
@ -648,9 +649,9 @@ static void GetBestGreenToRed(const uint32_t* argb, int stride, int tile_width,
const int kMaxIters = 4 + ((7 * quality) >> 8); // in range [4..6]
int green_to_red_best = 0;
int iter, offset;
float best_diff = GetPredictionCostCrossColorRed(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_red_best, accumulated_red_histo);
int64_t best_diff = GetPredictionCostCrossColorRed(
argb, stride, tile_width, tile_height, prev_x, prev_y, green_to_red_best,
accumulated_red_histo);
for (iter = 0; iter < kMaxIters; ++iter) {
// ColorTransformDelta is a 3.5 bit fixed point, so 32 is equal to
// one in color computation. Having initial delta here as 1 is sufficient
@ -659,7 +660,7 @@ static void GetBestGreenToRed(const uint32_t* argb, int stride, int tile_width,
// Try a negative and a positive delta from the best known value.
for (offset = -delta; offset <= delta; offset += 2 * delta) {
const int green_to_red_cur = offset + green_to_red_best;
const float cur_diff = GetPredictionCostCrossColorRed(
const int64_t cur_diff = GetPredictionCostCrossColorRed(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_red_cur, accumulated_red_histo);
if (cur_diff < best_diff) {
@ -671,34 +672,38 @@ static void GetBestGreenToRed(const uint32_t* argb, int stride, int tile_width,
best_tx->green_to_red_ = (green_to_red_best & 0xff);
}
static float GetPredictionCostCrossColorBlue(
static int64_t GetPredictionCostCrossColorBlue(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y, int green_to_blue,
int red_to_blue, const uint32_t accumulated_blue_histo[256]) {
uint32_t histo[256] = { 0 };
float cur_diff;
int64_t cur_diff;
VP8LCollectColorBlueTransforms(argb, stride, tile_width, tile_height,
green_to_blue, red_to_blue, histo);
cur_diff = PredictionCostCrossColor(accumulated_blue_histo, histo);
if ((uint8_t)green_to_blue == prev_x.green_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)green_to_blue == prev_y.green_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)red_to_blue == prev_x.red_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)red_to_blue == prev_y.red_to_blue_) {
cur_diff -= 3; // favor keeping the areas locally similar
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if (green_to_blue == 0) {
cur_diff -= 3;
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if (red_to_blue == 0) {
cur_diff -= 3;
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
return cur_diff;
}
@ -720,9 +725,9 @@ static void GetBestGreenRedToBlue(const uint32_t* argb, int stride,
int red_to_blue_best = 0;
int iter;
// Initial value at origin:
float best_diff = GetPredictionCostCrossColorBlue(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_blue_best, red_to_blue_best, accumulated_blue_histo);
int64_t best_diff = GetPredictionCostCrossColorBlue(
argb, stride, tile_width, tile_height, prev_x, prev_y, green_to_blue_best,
red_to_blue_best, accumulated_blue_histo);
for (iter = 0; iter < iters; ++iter) {
const int delta = delta_lut[iter];
int axis;
@ -730,7 +735,7 @@ static void GetBestGreenRedToBlue(const uint32_t* argb, int stride,
const int green_to_blue_cur =
offset[axis][0] * delta + green_to_blue_best;
const int red_to_blue_cur = offset[axis][1] * delta + red_to_blue_best;
const float cur_diff = GetPredictionCostCrossColorBlue(
const int64_t cur_diff = GetPredictionCostCrossColorBlue(
argb, stride, tile_width, tile_height, prev_x, prev_y,
green_to_blue_cur, red_to_blue_cur, accumulated_blue_histo);
if (cur_diff < best_diff) {