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diff --git a/src/3rdparty/libwebp/src/enc/analysis.c b/src/3rdparty/libwebp/src/enc/analysis.c
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+// 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;
+}
+
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