root/third_party/libwebp/enc/analysis.c

DEFINITIONS

1. SmoothSegmentMap
2. clip
3. SetSegmentAlphas
4. FinalAlphaValue
5. GetAlpha
6. MergeHistograms
7. AssignSegments
8. MBAnalyzeBestIntra16Mode
9. MBAnalyzeBestIntra4Mode
10. MBAnalyzeBestUVMode
11. MBAnalyze
12. DefaultMBInfo
13. ResetAllMBInfo
14. DoSegmentsJob
15. MergeJobs
16. InitSegmentJob
17. VP8EncAnalyze

// 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;
}