// 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) #include <stdlib.h> #include <string.h> #include <assert.h> #include "./vp8enci.h" #include "./cost.h" #include "../utils/utils.h" #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(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; break; } } 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]; } } WebPSafeFree(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; 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); } } //------------------------------------------------------------------------------ // 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) { // '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. const int max_value = histo->max_value; const int last_non_zero = histo->last_non_zero; const int alpha = (max_value > 1) ? ALPHA_SCALE * last_non_zero / max_value : 0; return alpha; } static void InitHistogram(VP8Histogram* const histo) { histo->max_value = 0; histo->last_non_zero = 1; } static void MergeHistograms(const VP8Histogram* const in, VP8Histogram* const out) { if (in->max_value > out->max_value) { out->max_value = in->max_value; } if (in->last_non_zero > out->last_non_zero) { out->last_non_zero = in->last_non_zero; } } //------------------------------------------------------------------------------ // Simplified k-Means, to assign Nb segments based on alpha-histogram static void AssignSegments(VP8Encoder* const enc, const int alphas[MAX_ALPHA + 1]) { // 'num_segments_' is previously validated and <= NUM_MB_SEGMENTS, but an // explicit check is needed to avoid spurious warning about 'n + 1' exceeding // array bounds of 'centers' with some compilers (noticed with gcc-4.9). const int nb = (enc->segment_hdr_.num_segments_ < NUM_MB_SEGMENTS) ? enc->segment_hdr_.num_segments_ : NUM_MB_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]; assert(nb >= 1); assert(nb <= 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 (k = 0, n = 1; k < nb; ++k, n += 2) { assert(n < 2 * nb); 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 + 1 < nb && 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. We don't need to test all // the possible modes during the analysis phase: we risk falling into a local // optimum, or be subject to boundary effect #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 = MAX_INTRA16_MODE; int mode; int best_alpha = DEFAULT_ALPHA; int best_mode = 0; VP8MakeLuma16Preds(it); for (mode = 0; mode < max_mode; ++mode) { VP8Histogram histo; int alpha; InitHistogram(&histo); VP8CollectHistogram(it->yuv_in_ + Y_OFF_ENC, 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 = MAX_INTRA4_MODE; int i4_alpha; VP8Histogram total_histo; int cur_histo = 0; InitHistogram(&total_histo); VP8IteratorStartI4(it); do { int mode; int best_mode_alpha = DEFAULT_ALPHA; VP8Histogram histos[2]; const uint8_t* const src = it->yuv_in_ + Y_OFF_ENC + VP8Scan[it->i4_]; VP8MakeIntra4Preds(it); for (mode = 0; mode < max_mode; ++mode) { int alpha; InitHistogram(&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_ENC)); 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 = MAX_UV_MODE; int mode; VP8MakeChroma8Preds(it); for (mode = 0; mode < max_mode; ++mode) { VP8Histogram histo; int alpha; InitHistogram(&histo); VP8CollectHistogram(it->yuv_in_ + U_OFF_ENC, 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_ -> set to default value. enc->alpha_ = 0; enc->uv_alpha_ = 0; WebPReportProgress(enc->pic_, enc->percent_ + 20, &enc->percent_); } // struct used to collect job result typedef struct { WebPWorker worker; int alphas[MAX_ALPHA + 1]; int alpha, uv_alpha; VP8EncIterator it; int delta_progress; } SegmentJob; // main work call static int DoSegmentsJob(SegmentJob* const job, VP8EncIterator* const it) { int ok = 1; if (!VP8IteratorIsDone(it)) { uint8_t tmp[32 + WEBP_ALIGN_CST]; uint8_t* const scratch = (uint8_t*)WEBP_ALIGN(tmp); do { // Let's pretend we have perfect lossless reconstruction. VP8IteratorImport(it, scratch); MBAnalyze(it, job->alphas, &job->alpha, &job->uv_alpha); ok = VP8IteratorProgress(it, job->delta_progress); } while (ok && VP8IteratorNext(it)); } return ok; } static void MergeJobs(const SegmentJob* const src, SegmentJob* const dst) { int i; for (i = 0; i <= MAX_ALPHA; ++i) dst->alphas[i] += src->alphas[i]; dst->alpha += src->alpha; dst->uv_alpha += src->uv_alpha; } // initialize the job struct with some TODOs static void InitSegmentJob(VP8Encoder* const enc, SegmentJob* const job, int start_row, int end_row) { WebPGetWorkerInterface()->Init(&job->worker); job->worker.data1 = job; job->worker.data2 = &job->it; job->worker.hook = (WebPWorkerHook)DoSegmentsJob; VP8IteratorInit(enc, &job->it); VP8IteratorSetRow(&job->it, start_row); VP8IteratorSetCountDown(&job->it, (end_row - start_row) * enc->mb_w_); memset(job->alphas, 0, sizeof(job->alphas)); job->alpha = 0; job->uv_alpha = 0; // only one of both jobs can record the progress, since we don't // expect the user's hook to be multi-thread safe job->delta_progress = (start_row == 0) ? 20 : 0; } // main entry point 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. if (do_segments) { const int last_row = enc->mb_h_; // We give a little more than a half work to the main thread. const int split_row = (9 * last_row + 15) >> 4; const int total_mb = last_row * enc->mb_w_; #ifdef WEBP_USE_THREAD const int kMinSplitRow = 2; // minimal rows needed for mt to be worth it const int do_mt = (enc->thread_level_ > 0) && (split_row >= kMinSplitRow); #else const int do_mt = 0; #endif const WebPWorkerInterface* const worker_interface = WebPGetWorkerInterface(); SegmentJob main_job; if (do_mt) { SegmentJob side_job; // Note the use of '&' instead of '&&' because we must call the functions // no matter what. InitSegmentJob(enc, &main_job, 0, split_row); InitSegmentJob(enc, &side_job, split_row, last_row); // we don't need to call Reset() on main_job.worker, since we're calling // WebPWorkerExecute() on it ok &= worker_interface->Reset(&side_job.worker); // launch the two jobs in parallel if (ok) { worker_interface->Launch(&side_job.worker); worker_interface->Execute(&main_job.worker); ok &= worker_interface->Sync(&side_job.worker); ok &= worker_interface->Sync(&main_job.worker); } worker_interface->End(&side_job.worker); if (ok) MergeJobs(&side_job, &main_job); // merge results together } else { // Even for single-thread case, we use the generic Worker tools. InitSegmentJob(enc, &main_job, 0, last_row); worker_interface->Execute(&main_job.worker); ok &= worker_interface->Sync(&main_job.worker); } worker_interface->End(&main_job.worker); if (ok) { enc->alpha_ = main_job.alpha / total_mb; enc->uv_alpha_ = main_job.uv_alpha / total_mb; AssignSegments(enc, main_job.alphas); } } else { // Use only one default segment. ResetAllMBInfo(enc); } return ok; }