// 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;
}
static void MergeHistograms(const VP8Histogram* const in,
VP8Histogram* const out) {
if (in->max_value > out->max_value) {
out->max_value = in->max_value;
}
if (in->last_non_zero > out->last_non_zero) {
out->last_non_zero = in->last_non_zero;
}
}
//------------------------------------------------------------------------------
// 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 MBAnalyzeBestIntra4Mode(VP8EncIterator* const it,
int best_alpha) {
uint8_t modes[16];
const int max_mode = MAX_INTRA4_MODE;
int i4_alpha;
VP8Histogram total_histo;
int cur_histo = 0;
InitHistogram(&total_histo);
VP8IteratorStartI4(it);
do {
int mode;
int best_mode_alpha = DEFAULT_ALPHA;
VP8Histogram histos[2];
const uint8_t* const src = it->yuv_in_ + Y_OFF_ENC + VP8Scan[it->i4_];
VP8MakeIntra4Preds(it);
for (mode = 0; mode < max_mode; ++mode) {
int alpha;
InitHistogram(&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_ENC));
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 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);
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(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;
}
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 = 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_;
// 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;
}