diff options
Diffstat (limited to 'drivers/webp/enc/analysis.c')
| -rw-r--r-- | drivers/webp/enc/analysis.c | 497 |
1 files changed, 497 insertions, 0 deletions
diff --git a/drivers/webp/enc/analysis.c b/drivers/webp/enc/analysis.c new file mode 100644 index 0000000000..7d4cfdc190 --- /dev/null +++ b/drivers/webp/enc/analysis.c @@ -0,0 +1,497 @@ +// 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((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; + 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]; + } + } + free(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) { + 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]; + + assert(nb >= 1); + + // 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. 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; + + memset(&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_ -> 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 + ALIGN_CST]; + uint8_t* const scratch = (uint8_t*)DO_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) { + WebPWorkerInit(&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 + 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 &= WebPWorkerReset(&side_job.worker); + // launch the two jobs in parallel + if (ok) { + WebPWorkerLaunch(&side_job.worker); + WebPWorkerExecute(&main_job.worker); + ok &= WebPWorkerSync(&side_job.worker); + ok &= WebPWorkerSync(&main_job.worker); + } + WebPWorkerEnd(&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); + WebPWorkerExecute(&main_job.worker); + ok &= WebPWorkerSync(&main_job.worker); + } + WebPWorkerEnd(&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; +} + |