// 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 "src/enc/vp8i_enc.h"

#include "src/enc/cost_enc.h"

#include "src/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;

}

//------------------------------------------------------------------------------

// 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 FastMBAnalyze(VP8EncIterator* const it) {

  // Empirical cut-off value, should be around 16 (~=block size). We use the

  // [8-17] range and favor intra4 at high quality, intra16 for low quality.

  const int q = (int)it->enc_->config_->quality;

  const uint32_t kThreshold = 8 + (17 - 8) * q / 100;

  int k;

  uint32_t dc[16], m, m2;

  for (k = 0; k < 16; k += 4) {

    VP8Mean16x4(it->yuv_in_ + Y_OFF_ENC + k * BPS, &dc[k]);

  }

  for (m = 0, m2 = 0, k = 0; k < 16; ++k) {

    m += dc[k];

    m2 += dc[k] * dc[k];

  }

  if (kThreshold * m2 < m * m) {

    VP8SetIntra16Mode(it, 0);   // DC16

  } else {

    const uint8_t modes[16] = { 0 };  // DC4

    VP8SetIntra4Mode(it, modes);

  }

  return 0;

}

static int MBAnalyzeBestUVMode(VP8EncIterator* const it) {

  int best_alpha = DEFAULT_ALPHA;

  int smallest_alpha = 0;

  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;

    }

    // The best prediction mode tends to be the one with the smallest alpha.

    if (mode == 0 || alpha < smallest_alpha) {

      smallest_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.

  if (enc->method_ <= 1) {

    best_alpha = FastMBAnalyze(it);

  } else {

    best_alpha = MBAnalyzeBestIntra16Mode(it);

  }

  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(void* arg1, void* arg2) {

  SegmentJob* const job = (SegmentJob*)arg1;

  VP8EncIterator* const it = (VP8EncIterator*)arg2;

  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;

}

#ifdef WEBP_USE_THREAD

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;

}

#endif

// initialize the job struct with some tasks to perform

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 = 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_ <= 1);  // for method 0 - 1, we need preds_[] to be filled.

  if (do_segments) {

    const int last_row = enc->mb_h_;

    const int total_mb = last_row * enc->mb_w_;

#ifdef WEBP_USE_THREAD

    // We give a little more than a half work to the main thread.

    const int split_row = (9 * last_row + 15) >> 4;

    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) {

#ifdef WEBP_USE_THREAD

      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

#endif  // WEBP_USE_THREAD

    } 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);

  }

  if (!ok) {

    return WebPEncodingSetError(enc->pic_,

                                VP8_ENC_ERROR_OUT_OF_MEMORY);  // imprecise

  }

  return ok;

}