365 lines
12 KiB
C
365 lines
12 KiB
C
// Copyright 2011 Google Inc. All Rights Reserved.
|
|
//
|
|
// This code is licensed under the same terms as WebM:
|
|
// Software License Agreement: http://www.webmproject.org/license/software/
|
|
// Additional IP Rights Grant: http://www.webmproject.org/license/additional/
|
|
// -----------------------------------------------------------------------------
|
|
//
|
|
// Macroblock analysis
|
|
//
|
|
// Author: Skal (pascal.massimino@gmail.com)
|
|
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
#include <assert.h>
|
|
|
|
#include "./vp8enci.h"
|
|
#include "./cost.h"
|
|
#include "../utils/utils.h"
|
|
|
|
#if defined(__cplusplus) || defined(c_plusplus)
|
|
extern "C" {
|
|
#endif
|
|
|
|
#define MAX_ITERS_K_MEANS 6
|
|
|
|
static int ClipAlpha(int alpha) {
|
|
return alpha < 0 ? 0 : alpha > 255 ? 255 : alpha;
|
|
}
|
|
|
|
//------------------------------------------------------------------------------
|
|
// Smooth the segment map by replacing isolated block by the majority of its
|
|
// neighbours.
|
|
|
|
static void SmoothSegmentMap(VP8Encoder* const enc) {
|
|
int n, x, y;
|
|
const int w = enc->mb_w_;
|
|
const int h = enc->mb_h_;
|
|
const int majority_cnt_3_x_3_grid = 5;
|
|
uint8_t* const tmp = (uint8_t*)WebPSafeMalloc((uint64_t)w * h, sizeof(*tmp));
|
|
assert((uint64_t)(w * h) == (uint64_t)w * h); // no overflow, as per spec
|
|
|
|
if (tmp == NULL) return;
|
|
for (y = 1; y < h - 1; ++y) {
|
|
for (x = 1; x < w - 1; ++x) {
|
|
int cnt[NUM_MB_SEGMENTS] = { 0 };
|
|
const VP8MBInfo* const mb = &enc->mb_info_[x + w * y];
|
|
int majority_seg = mb->segment_;
|
|
// Check the 8 neighbouring segment values.
|
|
cnt[mb[-w - 1].segment_]++; // top-left
|
|
cnt[mb[-w + 0].segment_]++; // top
|
|
cnt[mb[-w + 1].segment_]++; // top-right
|
|
cnt[mb[ - 1].segment_]++; // left
|
|
cnt[mb[ + 1].segment_]++; // right
|
|
cnt[mb[ w - 1].segment_]++; // bottom-left
|
|
cnt[mb[ w + 0].segment_]++; // bottom
|
|
cnt[mb[ w + 1].segment_]++; // bottom-right
|
|
for (n = 0; n < NUM_MB_SEGMENTS; ++n) {
|
|
if (cnt[n] >= majority_cnt_3_x_3_grid) {
|
|
majority_seg = n;
|
|
}
|
|
}
|
|
tmp[x + y * w] = majority_seg;
|
|
}
|
|
}
|
|
for (y = 1; y < h - 1; ++y) {
|
|
for (x = 1; x < w - 1; ++x) {
|
|
VP8MBInfo* const mb = &enc->mb_info_[x + w * y];
|
|
mb->segment_ = tmp[x + y * w];
|
|
}
|
|
}
|
|
free(tmp);
|
|
}
|
|
|
|
//------------------------------------------------------------------------------
|
|
// Finalize Segment probability based on the coding tree
|
|
|
|
static int GetProba(int a, int b) {
|
|
int proba;
|
|
const int total = a + b;
|
|
if (total == 0) return 255; // that's the default probability.
|
|
proba = (255 * a + total / 2) / total;
|
|
return proba;
|
|
}
|
|
|
|
static void SetSegmentProbas(VP8Encoder* const enc) {
|
|
int p[NUM_MB_SEGMENTS] = { 0 };
|
|
int n;
|
|
|
|
for (n = 0; n < enc->mb_w_ * enc->mb_h_; ++n) {
|
|
const VP8MBInfo* const mb = &enc->mb_info_[n];
|
|
p[mb->segment_]++;
|
|
}
|
|
if (enc->pic_->stats) {
|
|
for (n = 0; n < NUM_MB_SEGMENTS; ++n) {
|
|
enc->pic_->stats->segment_size[n] = p[n];
|
|
}
|
|
}
|
|
if (enc->segment_hdr_.num_segments_ > 1) {
|
|
uint8_t* const probas = enc->proba_.segments_;
|
|
probas[0] = GetProba(p[0] + p[1], p[2] + p[3]);
|
|
probas[1] = GetProba(p[0], p[1]);
|
|
probas[2] = GetProba(p[2], p[3]);
|
|
|
|
enc->segment_hdr_.update_map_ =
|
|
(probas[0] != 255) || (probas[1] != 255) || (probas[2] != 255);
|
|
enc->segment_hdr_.size_ =
|
|
p[0] * (VP8BitCost(0, probas[0]) + VP8BitCost(0, probas[1])) +
|
|
p[1] * (VP8BitCost(0, probas[0]) + VP8BitCost(1, probas[1])) +
|
|
p[2] * (VP8BitCost(1, probas[0]) + VP8BitCost(0, probas[2])) +
|
|
p[3] * (VP8BitCost(1, probas[0]) + VP8BitCost(1, probas[2]));
|
|
} else {
|
|
enc->segment_hdr_.update_map_ = 0;
|
|
enc->segment_hdr_.size_ = 0;
|
|
}
|
|
}
|
|
|
|
static WEBP_INLINE int clip(int v, int m, int M) {
|
|
return v < m ? m : v > M ? M : v;
|
|
}
|
|
|
|
static void SetSegmentAlphas(VP8Encoder* const enc,
|
|
const int centers[NUM_MB_SEGMENTS],
|
|
int mid) {
|
|
const int nb = enc->segment_hdr_.num_segments_;
|
|
int min = centers[0], max = centers[0];
|
|
int n;
|
|
|
|
if (nb > 1) {
|
|
for (n = 0; n < nb; ++n) {
|
|
if (min > centers[n]) min = centers[n];
|
|
if (max < centers[n]) max = centers[n];
|
|
}
|
|
}
|
|
if (max == min) max = min + 1;
|
|
assert(mid <= max && mid >= min);
|
|
for (n = 0; n < nb; ++n) {
|
|
const int alpha = 255 * (centers[n] - mid) / (max - min);
|
|
const int beta = 255 * (centers[n] - min) / (max - min);
|
|
enc->dqm_[n].alpha_ = clip(alpha, -127, 127);
|
|
enc->dqm_[n].beta_ = clip(beta, 0, 255);
|
|
}
|
|
}
|
|
|
|
//------------------------------------------------------------------------------
|
|
// Simplified k-Means, to assign Nb segments based on alpha-histogram
|
|
|
|
static void AssignSegments(VP8Encoder* const enc, const int alphas[256]) {
|
|
const int nb = enc->segment_hdr_.num_segments_;
|
|
int centers[NUM_MB_SEGMENTS];
|
|
int weighted_average = 0;
|
|
int map[256];
|
|
int a, n, k;
|
|
int min_a = 0, max_a = 255, range_a;
|
|
// 'int' type is ok for histo, and won't overflow
|
|
int accum[NUM_MB_SEGMENTS], dist_accum[NUM_MB_SEGMENTS];
|
|
|
|
// bracket the input
|
|
for (n = 0; n < 256 && alphas[n] == 0; ++n) {}
|
|
min_a = n;
|
|
for (n = 255; n > min_a && alphas[n] == 0; --n) {}
|
|
max_a = n;
|
|
range_a = max_a - min_a;
|
|
|
|
// Spread initial centers evenly
|
|
for (n = 1, k = 0; n < 2 * nb; n += 2) {
|
|
centers[k++] = min_a + (n * range_a) / (2 * nb);
|
|
}
|
|
|
|
for (k = 0; k < MAX_ITERS_K_MEANS; ++k) { // few iters are enough
|
|
int total_weight;
|
|
int displaced;
|
|
// Reset stats
|
|
for (n = 0; n < nb; ++n) {
|
|
accum[n] = 0;
|
|
dist_accum[n] = 0;
|
|
}
|
|
// Assign nearest center for each 'a'
|
|
n = 0; // track the nearest center for current 'a'
|
|
for (a = min_a; a <= max_a; ++a) {
|
|
if (alphas[a]) {
|
|
while (n < nb - 1 && abs(a - centers[n + 1]) < abs(a - centers[n])) {
|
|
n++;
|
|
}
|
|
map[a] = n;
|
|
// accumulate contribution into best centroid
|
|
dist_accum[n] += a * alphas[a];
|
|
accum[n] += alphas[a];
|
|
}
|
|
}
|
|
// All point are classified. Move the centroids to the
|
|
// center of their respective cloud.
|
|
displaced = 0;
|
|
weighted_average = 0;
|
|
total_weight = 0;
|
|
for (n = 0; n < nb; ++n) {
|
|
if (accum[n]) {
|
|
const int new_center = (dist_accum[n] + accum[n] / 2) / accum[n];
|
|
displaced += abs(centers[n] - new_center);
|
|
centers[n] = new_center;
|
|
weighted_average += new_center * accum[n];
|
|
total_weight += accum[n];
|
|
}
|
|
}
|
|
weighted_average = (weighted_average + total_weight / 2) / total_weight;
|
|
if (displaced < 5) break; // no need to keep on looping...
|
|
}
|
|
|
|
// Map each original value to the closest centroid
|
|
for (n = 0; n < enc->mb_w_ * enc->mb_h_; ++n) {
|
|
VP8MBInfo* const mb = &enc->mb_info_[n];
|
|
const int alpha = mb->alpha_;
|
|
mb->segment_ = map[alpha];
|
|
mb->alpha_ = centers[map[alpha]]; // just for the record.
|
|
}
|
|
|
|
if (nb > 1) {
|
|
const int smooth = (enc->config_->preprocessing & 1);
|
|
if (smooth) SmoothSegmentMap(enc);
|
|
}
|
|
|
|
SetSegmentProbas(enc); // Assign final proba
|
|
SetSegmentAlphas(enc, centers, weighted_average); // pick some alphas.
|
|
}
|
|
|
|
//------------------------------------------------------------------------------
|
|
// Macroblock analysis: collect histogram for each mode, deduce the maximal
|
|
// susceptibility and set best modes for this macroblock.
|
|
// Segment assignment is done later.
|
|
|
|
// Number of modes to inspect for alpha_ evaluation. For high-quality settings,
|
|
// we don't need to test all the possible modes during the analysis phase.
|
|
#define MAX_INTRA16_MODE 2
|
|
#define MAX_INTRA4_MODE 2
|
|
#define MAX_UV_MODE 2
|
|
|
|
static int MBAnalyzeBestIntra16Mode(VP8EncIterator* const it) {
|
|
const int max_mode = (it->enc_->method_ >= 3) ? MAX_INTRA16_MODE : 4;
|
|
int mode;
|
|
int best_alpha = -1;
|
|
int best_mode = 0;
|
|
|
|
VP8MakeLuma16Preds(it);
|
|
for (mode = 0; mode < max_mode; ++mode) {
|
|
const int alpha = VP8CollectHistogram(it->yuv_in_ + Y_OFF,
|
|
it->yuv_p_ + VP8I16ModeOffsets[mode],
|
|
0, 16);
|
|
if (alpha > best_alpha) {
|
|
best_alpha = alpha;
|
|
best_mode = mode;
|
|
}
|
|
}
|
|
VP8SetIntra16Mode(it, best_mode);
|
|
return best_alpha;
|
|
}
|
|
|
|
static int MBAnalyzeBestIntra4Mode(VP8EncIterator* const it,
|
|
int best_alpha) {
|
|
uint8_t modes[16];
|
|
const int max_mode = (it->enc_->method_ >= 3) ? MAX_INTRA4_MODE : NUM_BMODES;
|
|
int i4_alpha = 0;
|
|
VP8IteratorStartI4(it);
|
|
do {
|
|
int mode;
|
|
int best_mode_alpha = -1;
|
|
const uint8_t* const src = it->yuv_in_ + Y_OFF + VP8Scan[it->i4_];
|
|
|
|
VP8MakeIntra4Preds(it);
|
|
for (mode = 0; mode < max_mode; ++mode) {
|
|
const int alpha = VP8CollectHistogram(src,
|
|
it->yuv_p_ + VP8I4ModeOffsets[mode],
|
|
0, 1);
|
|
if (alpha > best_mode_alpha) {
|
|
best_mode_alpha = alpha;
|
|
modes[it->i4_] = mode;
|
|
}
|
|
}
|
|
i4_alpha += best_mode_alpha;
|
|
// Note: we reuse the original samples for predictors
|
|
} while (VP8IteratorRotateI4(it, it->yuv_in_ + Y_OFF));
|
|
|
|
if (i4_alpha > best_alpha) {
|
|
VP8SetIntra4Mode(it, modes);
|
|
best_alpha = ClipAlpha(i4_alpha);
|
|
}
|
|
return best_alpha;
|
|
}
|
|
|
|
static int MBAnalyzeBestUVMode(VP8EncIterator* const it) {
|
|
int best_alpha = -1;
|
|
int best_mode = 0;
|
|
const int max_mode = (it->enc_->method_ >= 3) ? MAX_UV_MODE : 4;
|
|
int mode;
|
|
VP8MakeChroma8Preds(it);
|
|
for (mode = 0; mode < max_mode; ++mode) {
|
|
const int alpha = VP8CollectHistogram(it->yuv_in_ + U_OFF,
|
|
it->yuv_p_ + VP8UVModeOffsets[mode],
|
|
16, 16 + 4 + 4);
|
|
if (alpha > best_alpha) {
|
|
best_alpha = alpha;
|
|
best_mode = mode;
|
|
}
|
|
}
|
|
VP8SetIntraUVMode(it, best_mode);
|
|
return best_alpha;
|
|
}
|
|
|
|
static void MBAnalyze(VP8EncIterator* const it,
|
|
int alphas[256], int* const uv_alpha) {
|
|
const VP8Encoder* const enc = it->enc_;
|
|
int best_alpha, best_uv_alpha;
|
|
|
|
VP8SetIntra16Mode(it, 0); // default: Intra16, DC_PRED
|
|
VP8SetSkip(it, 0); // not skipped
|
|
VP8SetSegment(it, 0); // default segment, spec-wise.
|
|
|
|
best_alpha = MBAnalyzeBestIntra16Mode(it);
|
|
if (enc->method_ != 3) {
|
|
// We go and make a fast decision for intra4/intra16.
|
|
// It's usually not a good and definitive pick, but helps seeding the stats
|
|
// about level bit-cost.
|
|
// TODO(skal): improve criterion.
|
|
best_alpha = MBAnalyzeBestIntra4Mode(it, best_alpha);
|
|
}
|
|
best_uv_alpha = MBAnalyzeBestUVMode(it);
|
|
|
|
// Final susceptibility mix
|
|
best_alpha = (best_alpha + best_uv_alpha + 1) / 2;
|
|
alphas[best_alpha]++;
|
|
*uv_alpha += best_uv_alpha;
|
|
it->mb_->alpha_ = best_alpha; // Informative only.
|
|
}
|
|
|
|
//------------------------------------------------------------------------------
|
|
// Main analysis loop:
|
|
// Collect all susceptibilities for each macroblock and record their
|
|
// distribution in alphas[]. Segments is assigned a-posteriori, based on
|
|
// this histogram.
|
|
// We also pick an intra16 prediction mode, which shouldn't be considered
|
|
// final except for fast-encode settings. We can also pick some intra4 modes
|
|
// and decide intra4/intra16, but that's usually almost always a bad choice at
|
|
// this stage.
|
|
|
|
int VP8EncAnalyze(VP8Encoder* const enc) {
|
|
int ok = 1;
|
|
int alphas[256] = { 0 };
|
|
VP8EncIterator it;
|
|
|
|
VP8IteratorInit(enc, &it);
|
|
enc->uv_alpha_ = 0;
|
|
do {
|
|
VP8IteratorImport(&it);
|
|
MBAnalyze(&it, alphas, &enc->uv_alpha_);
|
|
ok = VP8IteratorProgress(&it, 20);
|
|
// Let's pretend we have perfect lossless reconstruction.
|
|
} while (ok && VP8IteratorNext(&it, it.yuv_in_));
|
|
enc->uv_alpha_ /= enc->mb_w_ * enc->mb_h_;
|
|
if (ok) AssignSegments(enc, alphas);
|
|
|
|
return ok;
|
|
}
|
|
|
|
#if defined(__cplusplus) || defined(c_plusplus)
|
|
} // extern "C"
|
|
#endif
|