| // Copyright 2011 Google Inc. All Rights Reserved. |
| // |
| // Use of this source code is governed by a BSD-style license |
| // that can be found in the COPYING file in the root of the source |
| // tree. An additional intellectual property rights grant can be found |
| // in the file PATENTS. All contributing project authors may |
| // be found in the AUTHORS file in the root of the source tree. |
| // ----------------------------------------------------------------------------- |
| // |
| // Macroblock analysis |
| // |
| // Author: Skal (pascal.massimino@gmail.com) |
| |
| #if defined(STARBOARD) |
| #include "starboard/log.h" |
| #include "starboard/memory.h" |
| #else |
| #include <stdlib.h> |
| #include <string.h> |
| #include <assert.h> |
| #endif |
| |
| #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 |
| |
| //------------------------------------------------------------------------------ |
| // 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)); |
| SB_DCHECK((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]; |
| } |
| } |
| SbMemoryDeallocate(tmp); |
| } |
| |
| //------------------------------------------------------------------------------ |
| // set segment susceptibility alpha_ / beta_ |
| |
| 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; |
| SB_DCHECK(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); |
| } |
| } |
| |
| //------------------------------------------------------------------------------ |
| // Compute susceptibility based on DCT-coeff histograms: |
| // the higher, the "easier" the macroblock is to compress. |
| |
| #define MAX_ALPHA 255 // 8b of precision for susceptibilities. |
| #define ALPHA_SCALE (2 * MAX_ALPHA) // scaling factor for alpha. |
| #define DEFAULT_ALPHA (-1) |
| #define IS_BETTER_ALPHA(alpha, best_alpha) ((alpha) > (best_alpha)) |
| |
| static int FinalAlphaValue(int alpha) { |
| alpha = MAX_ALPHA - alpha; |
| return clip(alpha, 0, MAX_ALPHA); |
| } |
| |
| static int GetAlpha(const VP8Histogram* const histo) { |
| int max_value = 0, last_non_zero = 1; |
| int k; |
| int alpha; |
| for (k = 0; k <= MAX_COEFF_THRESH; ++k) { |
| const int value = histo->distribution[k]; |
| if (value > 0) { |
| if (value > max_value) max_value = value; |
| last_non_zero = k; |
| } |
| } |
| // 'alpha' will later be clipped to [0..MAX_ALPHA] range, clamping outer |
| // values which happen to be mostly noise. This leaves the maximum precision |
| // for handling the useful small values which contribute most. |
| alpha = (max_value > 1) ? ALPHA_SCALE * last_non_zero / max_value : 0; |
| return alpha; |
| } |
| |
| static void MergeHistograms(const VP8Histogram* const in, |
| VP8Histogram* const out) { |
| int i; |
| for (i = 0; i <= MAX_COEFF_THRESH; ++i) { |
| out->distribution[i] += in->distribution[i]; |
| } |
| } |
| |
| //------------------------------------------------------------------------------ |
| // Simplified k-Means, to assign Nb segments based on alpha-histogram |
| |
| static void AssignSegments(VP8Encoder* const enc, |
| const int alphas[MAX_ALPHA + 1]) { |
| const int nb = enc->segment_hdr_.num_segments_; |
| int centers[NUM_MB_SEGMENTS]; |
| int weighted_average = 0; |
| int map[MAX_ALPHA + 1]; |
| int a, n, k; |
| int min_a = 0, max_a = MAX_ALPHA, 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 <= MAX_ALPHA && alphas[n] == 0; ++n) {} |
| min_a = n; |
| for (n = MAX_ALPHA; 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]]; // for the record. |
| } |
| |
| if (nb > 1) { |
| const int smooth = (enc->config_->preprocessing & 1); |
| if (smooth) SmoothSegmentMap(enc); |
| } |
| |
| 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 |
| // (method >= FAST_ANALYSIS_METHOD) we don't need to test all the possible modes |
| // during the analysis phase. |
| #define FAST_ANALYSIS_METHOD 4 // method above which we do partial analysis |
| #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_ >= FAST_ANALYSIS_METHOD) ? MAX_INTRA16_MODE |
| : NUM_PRED_MODES; |
| int mode; |
| int best_alpha = DEFAULT_ALPHA; |
| int best_mode = 0; |
| |
| VP8MakeLuma16Preds(it); |
| for (mode = 0; mode < max_mode; ++mode) { |
| VP8Histogram histo = { { 0 } }; |
| int alpha; |
| |
| VP8CollectHistogram(it->yuv_in_ + Y_OFF, |
| it->yuv_p_ + VP8I16ModeOffsets[mode], |
| 0, 16, &histo); |
| alpha = GetAlpha(&histo); |
| if (IS_BETTER_ALPHA(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_ >= FAST_ANALYSIS_METHOD) ? MAX_INTRA4_MODE |
| : NUM_BMODES; |
| int i4_alpha; |
| VP8Histogram total_histo = { { 0 } }; |
| int cur_histo = 0; |
| |
| VP8IteratorStartI4(it); |
| do { |
| int mode; |
| int best_mode_alpha = DEFAULT_ALPHA; |
| VP8Histogram histos[2]; |
| const uint8_t* const src = it->yuv_in_ + Y_OFF + VP8Scan[it->i4_]; |
| |
| VP8MakeIntra4Preds(it); |
| for (mode = 0; mode < max_mode; ++mode) { |
| int alpha; |
| |
| SbMemorySet(&histos[cur_histo], 0, sizeof(histos[cur_histo])); |
| VP8CollectHistogram(src, it->yuv_p_ + VP8I4ModeOffsets[mode], |
| 0, 1, &histos[cur_histo]); |
| alpha = GetAlpha(&histos[cur_histo]); |
| if (IS_BETTER_ALPHA(alpha, best_mode_alpha)) { |
| best_mode_alpha = alpha; |
| modes[it->i4_] = mode; |
| cur_histo ^= 1; // keep track of best histo so far. |
| } |
| } |
| // accumulate best histogram |
| MergeHistograms(&histos[cur_histo ^ 1], &total_histo); |
| // Note: we reuse the original samples for predictors |
| } while (VP8IteratorRotateI4(it, it->yuv_in_ + Y_OFF)); |
| |
| i4_alpha = GetAlpha(&total_histo); |
| if (IS_BETTER_ALPHA(i4_alpha, best_alpha)) { |
| VP8SetIntra4Mode(it, modes); |
| best_alpha = i4_alpha; |
| } |
| return best_alpha; |
| } |
| |
| static int MBAnalyzeBestUVMode(VP8EncIterator* const it) { |
| int best_alpha = DEFAULT_ALPHA; |
| int best_mode = 0; |
| const int max_mode = |
| (it->enc_->method_ >= FAST_ANALYSIS_METHOD) ? MAX_UV_MODE |
| : NUM_PRED_MODES; |
| int mode; |
| VP8MakeChroma8Preds(it); |
| for (mode = 0; mode < max_mode; ++mode) { |
| VP8Histogram histo = { { 0 } }; |
| int alpha; |
| VP8CollectHistogram(it->yuv_in_ + U_OFF, |
| it->yuv_p_ + VP8UVModeOffsets[mode], |
| 16, 16 + 4 + 4, &histo); |
| alpha = GetAlpha(&histo); |
| if (IS_BETTER_ALPHA(alpha, best_alpha)) { |
| best_alpha = alpha; |
| best_mode = mode; |
| } |
| } |
| VP8SetIntraUVMode(it, best_mode); |
| return best_alpha; |
| } |
| |
| static void MBAnalyze(VP8EncIterator* const it, |
| int alphas[MAX_ALPHA + 1], |
| int* const alpha, 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_ >= 5) { |
| // 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 = (3 * best_alpha + best_uv_alpha + 2) >> 2; |
| best_alpha = FinalAlphaValue(best_alpha); |
| alphas[best_alpha]++; |
| it->mb_->alpha_ = best_alpha; // for later remapping. |
| |
| // Accumulate for later complexity analysis. |
| *alpha += best_alpha; // mixed susceptibility (not just luma) |
| *uv_alpha += best_uv_alpha; |
| } |
| |
| static void DefaultMBInfo(VP8MBInfo* const mb) { |
| mb->type_ = 1; // I16x16 |
| mb->uv_mode_ = 0; |
| mb->skip_ = 0; // not skipped |
| mb->segment_ = 0; // default segment |
| mb->alpha_ = 0; |
| } |
| |
| //------------------------------------------------------------------------------ |
| // 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. |
| |
| static void ResetAllMBInfo(VP8Encoder* const enc) { |
| int n; |
| for (n = 0; n < enc->mb_w_ * enc->mb_h_; ++n) { |
| DefaultMBInfo(&enc->mb_info_[n]); |
| } |
| // Default susceptibilities. |
| enc->dqm_[0].alpha_ = 0; |
| enc->dqm_[0].beta_ = 0; |
| // Note: we can't compute this alpha_ / uv_alpha_. |
| WebPReportProgress(enc->pic_, enc->percent_ + 20, &enc->percent_); |
| } |
| |
| int VP8EncAnalyze(VP8Encoder* const enc) { |
| int ok = 1; |
| const int do_segments = |
| enc->config_->emulate_jpeg_size || // We need the complexity evaluation. |
| (enc->segment_hdr_.num_segments_ > 1) || |
| (enc->method_ == 0); // for method 0, we need preds_[] to be filled. |
| enc->alpha_ = 0; |
| enc->uv_alpha_ = 0; |
| if (do_segments) { |
| int alphas[MAX_ALPHA + 1] = { 0 }; |
| VP8EncIterator it; |
| |
| VP8IteratorInit(enc, &it); |
| do { |
| VP8IteratorImport(&it); |
| MBAnalyze(&it, alphas, &enc->alpha_, &enc->uv_alpha_); |
| ok = VP8IteratorProgress(&it, 20); |
| // Let's pretend we have perfect lossless reconstruction. |
| } while (ok && VP8IteratorNext(&it, it.yuv_in_)); |
| enc->alpha_ /= enc->mb_w_ * enc->mb_h_; |
| enc->uv_alpha_ /= enc->mb_w_ * enc->mb_h_; |
| if (ok) AssignSegments(enc, alphas); |
| } else { // Use only one default segment. |
| ResetAllMBInfo(enc); |
| } |
| return ok; |
| } |
| |
| #if defined(__cplusplus) || defined(c_plusplus) |
| } // extern "C" |
| #endif |