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      1 /*
      2  *  Copyright (c) 2012 The WebM project authors. All Rights Reserved.
      3  *
      4  *  Use of this source code is governed by a BSD-style license
      5  *  that can be found in the LICENSE file in the root of the source
      6  *  tree. An additional intellectual property rights grant can be found
      7  *  in the file PATENTS.  All contributing project authors may
      8  *  be found in the AUTHORS file in the root of the source tree.
      9  */
     10 
     11 #include <limits.h>
     12 
     13 #include "vpx_mem/vpx_mem.h"
     14 
     15 #include "vp9/common/vp9_pred_common.h"
     16 #include "vp9/common/vp9_tile_common.h"
     17 
     18 #include "vp9/encoder/vp9_cost.h"
     19 #include "vp9/encoder/vp9_segmentation.h"
     20 
     21 void vp9_enable_segmentation(struct segmentation *seg) {
     22   seg->enabled = 1;
     23   seg->update_map = 1;
     24   seg->update_data = 1;
     25 }
     26 
     27 void vp9_disable_segmentation(struct segmentation *seg) {
     28   seg->enabled = 0;
     29   seg->update_map = 0;
     30   seg->update_data = 0;
     31 }
     32 
     33 void vp9_set_segment_data(struct segmentation *seg, signed char *feature_data,
     34                           unsigned char abs_delta) {
     35   seg->abs_delta = abs_delta;
     36 
     37   memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));
     38 }
     39 void vp9_disable_segfeature(struct segmentation *seg, int segment_id,
     40                             SEG_LVL_FEATURES feature_id) {
     41   seg->feature_mask[segment_id] &= ~(1 << feature_id);
     42 }
     43 
     44 void vp9_clear_segdata(struct segmentation *seg, int segment_id,
     45                        SEG_LVL_FEATURES feature_id) {
     46   seg->feature_data[segment_id][feature_id] = 0;
     47 }
     48 
     49 // Based on set of segment counts calculate a probability tree
     50 static void calc_segtree_probs(int *segcounts, vpx_prob *segment_tree_probs) {
     51   // Work out probabilities of each segment
     52   const int c01 = segcounts[0] + segcounts[1];
     53   const int c23 = segcounts[2] + segcounts[3];
     54   const int c45 = segcounts[4] + segcounts[5];
     55   const int c67 = segcounts[6] + segcounts[7];
     56 
     57   segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67);
     58   segment_tree_probs[1] = get_binary_prob(c01, c23);
     59   segment_tree_probs[2] = get_binary_prob(c45, c67);
     60   segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
     61   segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
     62   segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
     63   segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
     64 }
     65 
     66 // Based on set of segment counts and probabilities calculate a cost estimate
     67 static int cost_segmap(int *segcounts, vpx_prob *probs) {
     68   const int c01 = segcounts[0] + segcounts[1];
     69   const int c23 = segcounts[2] + segcounts[3];
     70   const int c45 = segcounts[4] + segcounts[5];
     71   const int c67 = segcounts[6] + segcounts[7];
     72   const int c0123 = c01 + c23;
     73   const int c4567 = c45 + c67;
     74 
     75   // Cost the top node of the tree
     76   int cost = c0123 * vp9_cost_zero(probs[0]) + c4567 * vp9_cost_one(probs[0]);
     77 
     78   // Cost subsequent levels
     79   if (c0123 > 0) {
     80     cost += c01 * vp9_cost_zero(probs[1]) + c23 * vp9_cost_one(probs[1]);
     81 
     82     if (c01 > 0)
     83       cost += segcounts[0] * vp9_cost_zero(probs[3]) +
     84               segcounts[1] * vp9_cost_one(probs[3]);
     85     if (c23 > 0)
     86       cost += segcounts[2] * vp9_cost_zero(probs[4]) +
     87               segcounts[3] * vp9_cost_one(probs[4]);
     88   }
     89 
     90   if (c4567 > 0) {
     91     cost += c45 * vp9_cost_zero(probs[2]) + c67 * vp9_cost_one(probs[2]);
     92 
     93     if (c45 > 0)
     94       cost += segcounts[4] * vp9_cost_zero(probs[5]) +
     95               segcounts[5] * vp9_cost_one(probs[5]);
     96     if (c67 > 0)
     97       cost += segcounts[6] * vp9_cost_zero(probs[6]) +
     98               segcounts[7] * vp9_cost_one(probs[6]);
     99   }
    100 
    101   return cost;
    102 }
    103 
    104 static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd,
    105                        const TileInfo *tile, MODE_INFO **mi,
    106                        int *no_pred_segcounts,
    107                        int (*temporal_predictor_count)[2],
    108                        int *t_unpred_seg_counts, int bw, int bh, int mi_row,
    109                        int mi_col) {
    110   int segment_id;
    111 
    112   if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;
    113 
    114   xd->mi = mi;
    115   segment_id = xd->mi[0]->segment_id;
    116 
    117   set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols);
    118 
    119   // Count the number of hits on each segment with no prediction
    120   no_pred_segcounts[segment_id]++;
    121 
    122   // Temporal prediction not allowed on key frames
    123   if (cm->frame_type != KEY_FRAME) {
    124     const BLOCK_SIZE bsize = xd->mi[0]->sb_type;
    125     // Test to see if the segment id matches the predicted value.
    126     const int pred_segment_id =
    127         get_segment_id(cm, cm->last_frame_seg_map, bsize, mi_row, mi_col);
    128     const int pred_flag = pred_segment_id == segment_id;
    129     const int pred_context = vp9_get_pred_context_seg_id(xd);
    130 
    131     // Store the prediction status for this mb and update counts
    132     // as appropriate
    133     xd->mi[0]->seg_id_predicted = pred_flag;
    134     temporal_predictor_count[pred_context][pred_flag]++;
    135 
    136     // Update the "unpredicted" segment count
    137     if (!pred_flag) t_unpred_seg_counts[segment_id]++;
    138   }
    139 }
    140 
    141 static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd,
    142                           const TileInfo *tile, MODE_INFO **mi,
    143                           int *no_pred_segcounts,
    144                           int (*temporal_predictor_count)[2],
    145                           int *t_unpred_seg_counts, int mi_row, int mi_col,
    146                           BLOCK_SIZE bsize) {
    147   const int mis = cm->mi_stride;
    148   int bw, bh;
    149   const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2;
    150 
    151   if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;
    152 
    153   bw = num_8x8_blocks_wide_lookup[mi[0]->sb_type];
    154   bh = num_8x8_blocks_high_lookup[mi[0]->sb_type];
    155 
    156   if (bw == bs && bh == bs) {
    157     count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
    158                t_unpred_seg_counts, bs, bs, mi_row, mi_col);
    159   } else if (bw == bs && bh < bs) {
    160     count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
    161                t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
    162     count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
    163                temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
    164                mi_row + hbs, mi_col);
    165   } else if (bw < bs && bh == bs) {
    166     count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
    167                t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
    168     count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
    169                temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
    170                mi_col + hbs);
    171   } else {
    172     const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
    173     int n;
    174 
    175     assert(bw < bs && bh < bs);
    176 
    177     for (n = 0; n < 4; n++) {
    178       const int mi_dc = hbs * (n & 1);
    179       const int mi_dr = hbs * (n >> 1);
    180 
    181       count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts,
    182                     temporal_predictor_count, t_unpred_seg_counts,
    183                     mi_row + mi_dr, mi_col + mi_dc, subsize);
    184     }
    185   }
    186 }
    187 
    188 void vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) {
    189   struct segmentation *seg = &cm->seg;
    190 
    191   int no_pred_cost;
    192   int t_pred_cost = INT_MAX;
    193 
    194   int i, tile_col, mi_row, mi_col;
    195 
    196   int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };
    197   int no_pred_segcounts[MAX_SEGMENTS] = { 0 };
    198   int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
    199 
    200   vpx_prob no_pred_tree[SEG_TREE_PROBS];
    201   vpx_prob t_pred_tree[SEG_TREE_PROBS];
    202   vpx_prob t_nopred_prob[PREDICTION_PROBS];
    203 
    204   // Set default state for the segment tree probabilities and the
    205   // temporal coding probabilities
    206   memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
    207   memset(seg->pred_probs, 255, sizeof(seg->pred_probs));
    208 
    209   // First of all generate stats regarding how well the last segment map
    210   // predicts this one
    211   for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {
    212     TileInfo tile;
    213     MODE_INFO **mi_ptr;
    214     vp9_tile_init(&tile, cm, 0, tile_col);
    215 
    216     mi_ptr = cm->mi_grid_visible + tile.mi_col_start;
    217     for (mi_row = 0; mi_row < cm->mi_rows;
    218          mi_row += 8, mi_ptr += 8 * cm->mi_stride) {
    219       MODE_INFO **mi = mi_ptr;
    220       for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;
    221            mi_col += 8, mi += 8)
    222         count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,
    223                       temporal_predictor_count, t_unpred_seg_counts, mi_row,
    224                       mi_col, BLOCK_64X64);
    225     }
    226   }
    227 
    228   // Work out probability tree for coding segments without prediction
    229   // and the cost.
    230   calc_segtree_probs(no_pred_segcounts, no_pred_tree);
    231   no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
    232 
    233   // Key frames cannot use temporal prediction
    234   if (!frame_is_intra_only(cm)) {
    235     // Work out probability tree for coding those segments not
    236     // predicted using the temporal method and the cost.
    237     calc_segtree_probs(t_unpred_seg_counts, t_pred_tree);
    238     t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
    239 
    240     // Add in the cost of the signaling for each prediction context.
    241     for (i = 0; i < PREDICTION_PROBS; i++) {
    242       const int count0 = temporal_predictor_count[i][0];
    243       const int count1 = temporal_predictor_count[i][1];
    244 
    245       t_nopred_prob[i] = get_binary_prob(count0, count1);
    246 
    247       // Add in the predictor signaling cost
    248       t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +
    249                      count1 * vp9_cost_one(t_nopred_prob[i]);
    250     }
    251   }
    252 
    253   // Now choose which coding method to use.
    254   if (t_pred_cost < no_pred_cost) {
    255     seg->temporal_update = 1;
    256     memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree));
    257     memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
    258   } else {
    259     seg->temporal_update = 0;
    260     memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree));
    261   }
    262 }
    263 
    264 void vp9_reset_segment_features(struct segmentation *seg) {
    265   // Set up default state for MB feature flags
    266   seg->enabled = 0;
    267   seg->update_map = 0;
    268   seg->update_data = 0;
    269   memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
    270   vp9_clearall_segfeatures(seg);
    271 }
    272