/* *  Copyright (c) 2012 The WebM project authors. All Rights Reserved. * *  Use of this source code is governed by a BSD-style license *  that can be found in the LICENSE 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. */ #include <limits.h>#include <math.h> #include "vpx_mem/vpx_mem.h" #include "vp9/common/vp9_pred_common.h"#include "vp9/common/vp9_tile_common.h" #include "vp9/encoder/vp9_cost.h"#include "vp9/encoder/vp9_segmentation.h" void vp9_enable_segmentation(struct segmentation *seg) {  seg->enabled = 1;  seg->update_map = 1;  seg->update_data = 1;} void vp9_disable_segmentation(struct segmentation *seg) {  seg->enabled = 0;  seg->update_map = 0;  seg->update_data = 0;} void vp9_set_segment_data(struct segmentation *seg, signed char *feature_data,                          unsigned char abs_delta) {  seg->abs_delta = abs_delta;   memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));}void vp9_disable_segfeature(struct segmentation *seg, int segment_id,                            SEG_LVL_FEATURES feature_id) {  seg->feature_mask[segment_id] &= ~(1 << feature_id);} void vp9_clear_segdata(struct segmentation *seg, int segment_id,                       SEG_LVL_FEATURES feature_id) {  seg->feature_data[segment_id][feature_id] = 0;} void vp9_psnr_aq_mode_setup(struct segmentation *seg) {  int i;   vp9_enable_segmentation(seg);  vp9_clearall_segfeatures(seg);  seg->abs_delta = SEGMENT_DELTADATA;   for (i = 0; i < MAX_SEGMENTS; ++i) {    vp9_set_segdata(seg, i, SEG_LVL_ALT_Q, 2 * (i - (MAX_SEGMENTS / 2)));    vp9_enable_segfeature(seg, i, SEG_LVL_ALT_Q);  }} void vp9_perceptual_aq_mode_setup(struct VP9_COMP *cpi,                                  struct segmentation *seg) {  const VP9_COMMON *cm = &cpi->common;  const int seg_counts = cpi->kmeans_ctr_num;  const int base_qindex = cm->base_qindex;  const double base_qstep = vp9_convert_qindex_to_q(base_qindex, cm->bit_depth);  const double mid_ctr = cpi->kmeans_ctr_ls[seg_counts / 2];  const double var_diff_scale = 4.0;  int i;   assert(seg_counts <= MAX_SEGMENTS);   vp9_enable_segmentation(seg);  vp9_clearall_segfeatures(seg);  seg->abs_delta = SEGMENT_DELTADATA;   for (i = 0; i < seg_counts / 2; ++i) {    double wiener_var_diff = mid_ctr - cpi->kmeans_ctr_ls[i];    double target_qstep = base_qstep / (1.0 + wiener_var_diff / var_diff_scale);    int target_qindex = vp9_convert_q_to_qindex(target_qstep, cm->bit_depth);    assert(wiener_var_diff >= 0.0);     vp9_set_segdata(seg, i, SEG_LVL_ALT_Q, target_qindex - base_qindex);    vp9_enable_segfeature(seg, i, SEG_LVL_ALT_Q);  }   vp9_set_segdata(seg, i, SEG_LVL_ALT_Q, 0);  vp9_enable_segfeature(seg, i, SEG_LVL_ALT_Q);   for (; i < seg_counts; ++i) {    double wiener_var_diff = cpi->kmeans_ctr_ls[i] - mid_ctr;    double target_qstep = base_qstep * (1.0 + wiener_var_diff / var_diff_scale);    int target_qindex = vp9_convert_q_to_qindex(target_qstep, cm->bit_depth);    assert(wiener_var_diff >= 0.0);     vp9_set_segdata(seg, i, SEG_LVL_ALT_Q, target_qindex - base_qindex);    vp9_enable_segfeature(seg, i, SEG_LVL_ALT_Q);  }} // Based on set of segment counts calculate a probability treestatic void calc_segtree_probs(int *segcounts, vpx_prob *segment_tree_probs) {  // Work out probabilities of each segment  const int c01 = segcounts[0] + segcounts[1];  const int c23 = segcounts[2] + segcounts[3];  const int c45 = segcounts[4] + segcounts[5];  const int c67 = segcounts[6] + segcounts[7];   segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67);  segment_tree_probs[1] = get_binary_prob(c01, c23);  segment_tree_probs[2] = get_binary_prob(c45, c67);  segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);  segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);  segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);  segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);} // Based on set of segment counts and probabilities calculate a cost estimatestatic int cost_segmap(int *segcounts, vpx_prob *probs) {  const int c01 = segcounts[0] + segcounts[1];  const int c23 = segcounts[2] + segcounts[3];  const int c45 = segcounts[4] + segcounts[5];  const int c67 = segcounts[6] + segcounts[7];  const int c0123 = c01 + c23;  const int c4567 = c45 + c67;   // Cost the top node of the tree  int cost = c0123 * vp9_cost_zero(probs[0]) + c4567 * vp9_cost_one(probs[0]);   // Cost subsequent levels  if (c0123 > 0) {    cost += c01 * vp9_cost_zero(probs[1]) + c23 * vp9_cost_one(probs[1]);     if (c01 > 0)      cost += segcounts[0] * vp9_cost_zero(probs[3]) +              segcounts[1] * vp9_cost_one(probs[3]);    if (c23 > 0)      cost += segcounts[2] * vp9_cost_zero(probs[4]) +              segcounts[3] * vp9_cost_one(probs[4]);  }   if (c4567 > 0) {    cost += c45 * vp9_cost_zero(probs[2]) + c67 * vp9_cost_one(probs[2]);     if (c45 > 0)      cost += segcounts[4] * vp9_cost_zero(probs[5]) +              segcounts[5] * vp9_cost_one(probs[5]);    if (c67 > 0)      cost += segcounts[6] * vp9_cost_zero(probs[6]) +              segcounts[7] * vp9_cost_one(probs[6]);  }   return cost;} static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd,                       const TileInfo *tile, MODE_INFO **mi,                       int *no_pred_segcounts,                       int (*temporal_predictor_count)[2],                       int *t_unpred_seg_counts, int bw, int bh, int mi_row,                       int mi_col) {  int segment_id;   if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;   xd->mi = mi;  segment_id = xd->mi[0]->segment_id;   set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols);   // Count the number of hits on each segment with no prediction  no_pred_segcounts[segment_id]++;   // Temporal prediction not allowed on key frames  if (cm->frame_type != KEY_FRAME) {    const BLOCK_SIZE bsize = xd->mi[0]->sb_type;    // Test to see if the segment id matches the predicted value.    const int pred_segment_id =        get_segment_id(cm, cm->last_frame_seg_map, bsize, mi_row, mi_col);    const int pred_flag = pred_segment_id == segment_id;    const int pred_context = vp9_get_pred_context_seg_id(xd);     // Store the prediction status for this mb and update counts    // as appropriate    xd->mi[0]->seg_id_predicted = pred_flag;    temporal_predictor_count[pred_context][pred_flag]++;     // Update the "unpredicted" segment count    if (!pred_flag) t_unpred_seg_counts[segment_id]++;  }} static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd,                          const TileInfo *tile, MODE_INFO **mi,                          int *no_pred_segcounts,                          int (*temporal_predictor_count)[2],                          int *t_unpred_seg_counts, int mi_row, int mi_col,                          BLOCK_SIZE bsize) {  const int mis = cm->mi_stride;  int bw, bh;  const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2;   if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;   bw = num_8x8_blocks_wide_lookup[mi[0]->sb_type];  bh = num_8x8_blocks_high_lookup[mi[0]->sb_type];   if (bw == bs && bh == bs) {    count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,               t_unpred_seg_counts, bs, bs, mi_row, mi_col);  } else if (bw == bs && bh < bs) {    count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,               t_unpred_seg_counts, bs, hbs, mi_row, mi_col);    count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,               temporal_predictor_count, t_unpred_seg_counts, bs, hbs,               mi_row + hbs, mi_col);  } else if (bw < bs && bh == bs) {    count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,               t_unpred_seg_counts, hbs, bs, mi_row, mi_col);    count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,               temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,               mi_col + hbs);  } else {    const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];    int n;     assert(bw < bs && bh < bs);     for (n = 0; n < 4; n++) {      const int mi_dc = hbs * (n & 1);      const int mi_dr = hbs * (n >> 1);       count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts,                    temporal_predictor_count, t_unpred_seg_counts,                    mi_row + mi_dr, mi_col + mi_dc, subsize);    }  }} void vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) {  struct segmentation *seg = &cm->seg;   int no_pred_cost;  int t_pred_cost = INT_MAX;   int i, tile_col, mi_row, mi_col;   int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };  int no_pred_segcounts[MAX_SEGMENTS] = { 0 };  int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };   vpx_prob no_pred_tree[SEG_TREE_PROBS];  vpx_prob t_pred_tree[SEG_TREE_PROBS];  vpx_prob t_nopred_prob[PREDICTION_PROBS];   // Set default state for the segment tree probabilities and the  // temporal coding probabilities  memset(seg->tree_probs, 255, sizeof(seg->tree_probs));  memset(seg->pred_probs, 255, sizeof(seg->pred_probs));   // First of all generate stats regarding how well the last segment map  // predicts this one  for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {    TileInfo tile;    MODE_INFO **mi_ptr;    vp9_tile_init(&tile, cm, 0, tile_col);     mi_ptr = cm->mi_grid_visible + tile.mi_col_start;    for (mi_row = 0; mi_row < cm->mi_rows;         mi_row += 8, mi_ptr += 8 * cm->mi_stride) {      MODE_INFO **mi = mi_ptr;      for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;           mi_col += 8, mi += 8)        count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,                      temporal_predictor_count, t_unpred_seg_counts, mi_row,                      mi_col, BLOCK_64X64);    }  }   // Work out probability tree for coding segments without prediction  // and the cost.  calc_segtree_probs(no_pred_segcounts, no_pred_tree);  no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);   // Key frames cannot use temporal prediction  if (!frame_is_intra_only(cm)) {    // Work out probability tree for coding those segments not    // predicted using the temporal method and the cost.    calc_segtree_probs(t_unpred_seg_counts, t_pred_tree);    t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);     // Add in the cost of the signaling for each prediction context.    for (i = 0; i < PREDICTION_PROBS; i++) {      const int count0 = temporal_predictor_count[i][0];      const int count1 = temporal_predictor_count[i][1];       t_nopred_prob[i] = get_binary_prob(count0, count1);       // Add in the predictor signaling cost      t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +                     count1 * vp9_cost_one(t_nopred_prob[i]);    }  }   // Now choose which coding method to use.  if (t_pred_cost < no_pred_cost) {    seg->temporal_update = 1;    memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree));    memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));  } else {    seg->temporal_update = 0;    memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree));  }} void vp9_reset_segment_features(struct segmentation *seg) {  // Set up default state for MB feature flags  seg->enabled = 0;  seg->update_map = 0;  seg->update_data = 0;  memset(seg->tree_probs, 255, sizeof(seg->tree_probs));  vp9_clearall_segfeatures(seg);}