Use the upstream version of libwebp, v0.4.3.

third_party/libwebp/enc/analysis.c

Index: third_party/libwebp/enc/analysis.c
diff --git a/third_party/libwebp/enc/analysis.c b/third_party/libwebp/enc/analysis.c
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index 0000000000000000000000000000000000000000..e019465bbabdec0c88190e0a16f3add270abde1f
--- /dev/null
+++ b/third_party/libwebp/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(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) {
+  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]) {
+  // '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 = { { 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 = MAX_INTRA4_MODE;
+  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 = MAX_UV_MODE;
+  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) {
+  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;
+}
+