vp9/encoder/vp9_segmentation.c

   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
  12 #include <limits.h>
  13
  14 #include "vpx_mem/vpx_mem.h"
  15
  16 #include "vp9/common/vp9_pred_common.h"
  17 #include "vp9/common/vp9_tile_common.h"
  18
  19 #include "vp9/encoder/vp9_cost.h"
  20 #include "vp9/encoder/vp9_segmentation.h"
  21
  22 void vp9_enable_segmentation(struct segmentation *seg) {
  23   seg->enabled = 1;
  24   seg->update_map = 1;
  25   seg->update_data = 1;
  26 }
  27
  28 void vp9_disable_segmentation(struct segmentation *seg) {
  29   seg->enabled = 0;
  30   seg->update_map = 0;
  31   seg->update_data = 0;
  32 }
  33
  34 void vp9_set_segment_data(struct segmentation *seg,
  35                           signed char *feature_data,
  36                           unsigned char abs_delta) {
  37   seg->abs_delta = abs_delta;
  38
  39   memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));
  40 }
  41 void vp9_disable_segfeature(struct segmentation *seg, int segment_id,
  42                             SEG_LVL_FEATURES feature_id) {
  43   seg->feature_mask[segment_id] &= ~(1 << feature_id);
  44 }
  45
  46 void vp9_clear_segdata(struct segmentation *seg, int segment_id,
  47                        SEG_LVL_FEATURES feature_id) {
  48   seg->feature_data[segment_id][feature_id] = 0;
  49 }
  50
  51 // Based on set of segment counts calculate a probability tree
  52 static void calc_segtree_probs(int *segcounts, vp9_prob *segment_tree_probs) {
  53   // Work out probabilities of each segment
  54   const int c01 = segcounts[0] + segcounts[1];
  55   const int c23 = segcounts[2] + segcounts[3];
  56   const int c45 = segcounts[4] + segcounts[5];
  57   const int c67 = segcounts[6] + segcounts[7];
  58
  59   segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67);
  60   segment_tree_probs[1] = get_binary_prob(c01, c23);
  61   segment_tree_probs[2] = get_binary_prob(c45, c67);
  62   segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
  63   segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
  64   segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
  65   segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
  66 }
  67
  68 // Based on set of segment counts and probabilities calculate a cost estimate
  69 static int cost_segmap(int *segcounts, vp9_prob *probs) {
  70   const int c01 = segcounts[0] + segcounts[1];
  71   const int c23 = segcounts[2] + segcounts[3];
  72   const int c45 = segcounts[4] + segcounts[5];
  73   const int c67 = segcounts[6] + segcounts[7];
  74   const int c0123 = c01 + c23;
  75   const int c4567 = c45 + c67;
  76
  77   // Cost the top node of the tree
  78   int cost = c0123 * vp9_cost_zero(probs[0]) +
  79              c4567 * vp9_cost_one(probs[0]);
  80
  81   // Cost subsequent levels
  82   if (c0123 > 0) {
  83     cost += c01 * vp9_cost_zero(probs[1]) +
  84             c23 * vp9_cost_one(probs[1]);
  85
  86     if (c01 > 0)
  87       cost += segcounts[0] * vp9_cost_zero(probs[3]) +
  88               segcounts[1] * vp9_cost_one(probs[3]);
  89     if (c23 > 0)
  90       cost += segcounts[2] * vp9_cost_zero(probs[4]) +
  91               segcounts[3] * vp9_cost_one(probs[4]);
  92   }
  93
  94   if (c4567 > 0) {
  95     cost += c45 * vp9_cost_zero(probs[2]) +
  96             c67 * vp9_cost_one(probs[2]);
  97
  98     if (c45 > 0)
  99       cost += segcounts[4] * vp9_cost_zero(probs[5]) +
 100               segcounts[5] * vp9_cost_one(probs[5]);
 101     if (c67 > 0)
 102       cost += segcounts[6] * vp9_cost_zero(probs[6]) +
 103               segcounts[7] * vp9_cost_one(probs[6]);
 104   }
 105
 106   return cost;
 107 }
 108
 109 static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd,
 110                        const TileInfo *tile, MODE_INFO **mi,
 111                        int *no_pred_segcounts,
 112                        int (*temporal_predictor_count)[2],
 113                        int *t_unpred_seg_counts,
 114                        int bw, int bh, int mi_row, int mi_col) {
 115   int segment_id;
 116
 117   if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
 118     return;
 119
 120   xd->mi = mi;
 121   segment_id = xd->mi[0]->mbmi.segment_id;
 122
 123   set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols);
 124
 125   // Count the number of hits on each segment with no prediction
 126   no_pred_segcounts[segment_id]++;
 127
 128   // Temporal prediction not allowed on key frames
 129   if (cm->frame_type != KEY_FRAME) {
 130     const BLOCK_SIZE bsize = xd->mi[0]->mbmi.sb_type;
 131     // Test to see if the segment id matches the predicted value.
 132     const int pred_segment_id = get_segment_id(cm, cm->last_frame_seg_map,
 133                                                bsize, mi_row, mi_col);
 134     const int pred_flag = pred_segment_id == segment_id;
 135     const int pred_context = vp9_get_pred_context_seg_id(xd);
 136
 137     // Store the prediction status for this mb and update counts
 138     // as appropriate
 139     xd->mi[0]->mbmi.seg_id_predicted = pred_flag;
 140     temporal_predictor_count[pred_context][pred_flag]++;
 141
 142     // Update the "unpredicted" segment count
 143     if (!pred_flag)
 144       t_unpred_seg_counts[segment_id]++;
 145   }
 146 }
 147
 148 static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd,
 149                           const TileInfo *tile, MODE_INFO **mi,
 150                           int *no_pred_segcounts,
 151                           int (*temporal_predictor_count)[2],
 152                           int *t_unpred_seg_counts,
 153                           int mi_row, int mi_col,
 154                           BLOCK_SIZE bsize) {
 155   const int mis = cm->mi_stride;
 156   int bw, bh;
 157   const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2;
 158
 159   if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
 160     return;
 161
 162   bw = num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type];
 163   bh = num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type];
 164
 165   if (bw == bs && bh == bs) {
 166     count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
 167                t_unpred_seg_counts, bs, bs, mi_row, mi_col);
 168   } else if (bw == bs && bh < bs) {
 169     count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
 170                t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
 171     count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
 172                temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
 173                mi_row + hbs, mi_col);
 174   } else if (bw < bs && bh == bs) {
 175     count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
 176                t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
 177     count_segs(cm, xd, tile, mi + hbs,
 178                no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts,
 179                hbs, bs, mi_row, mi_col + hbs);
 180   } else {
 181     const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
 182     int n;
 183
 184     assert(bw < bs && bh < bs);
 185
 186     for (n = 0; n < 4; n++) {
 187       const int mi_dc = hbs * (n & 1);
 188       const int mi_dr = hbs * (n >> 1);
 189
 190       count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc],
 191                     no_pred_segcounts, temporal_predictor_count,
 192                     t_unpred_seg_counts,
 193                     mi_row + mi_dr, mi_col + mi_dc, subsize);
 194     }
 195   }
 196 }
 197
 198 void vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) {
 199   struct segmentation *seg = &cm->seg;
 200
 201   int no_pred_cost;
 202   int t_pred_cost = INT_MAX;
 203
 204   int i, tile_col, mi_row, mi_col;
 205
 206   int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };
 207   int no_pred_segcounts[MAX_SEGMENTS] = { 0 };
 208   int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
 209
 210   vp9_prob no_pred_tree[SEG_TREE_PROBS];
 211   vp9_prob t_pred_tree[SEG_TREE_PROBS];
 212   vp9_prob t_nopred_prob[PREDICTION_PROBS];
 213
 214   // Set default state for the segment tree probabilities and the
 215   // temporal coding probabilities
 216   memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
 217   memset(seg->pred_probs, 255, sizeof(seg->pred_probs));
 218
 219   // First of all generate stats regarding how well the last segment map
 220   // predicts this one
 221   for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {
 222     TileInfo tile;
 223     MODE_INFO **mi_ptr;
 224     vp9_tile_init(&tile, cm, 0, tile_col);
 225
 226     mi_ptr = cm->mi_grid_visible + tile.mi_col_start;
 227     for (mi_row = 0; mi_row < cm->mi_rows;
 228          mi_row += 8, mi_ptr += 8 * cm->mi_stride) {
 229       MODE_INFO **mi = mi_ptr;
 230       for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;
 231            mi_col += 8, mi += 8)
 232         count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,
 233                       temporal_predictor_count, t_unpred_seg_counts,
 234                       mi_row, mi_col, BLOCK_64X64);
 235     }
 236   }
 237
 238   // Work out probability tree for coding segments without prediction
 239   // and the cost.
 240   calc_segtree_probs(no_pred_segcounts, no_pred_tree);
 241   no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
 242
 243   // Key frames cannot use temporal prediction
 244   if (!frame_is_intra_only(cm)) {
 245     // Work out probability tree for coding those segments not
 246     // predicted using the temporal method and the cost.
 247     calc_segtree_probs(t_unpred_seg_counts, t_pred_tree);
 248     t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
 249
 250     // Add in the cost of the signaling for each prediction context.
 251     for (i = 0; i < PREDICTION_PROBS; i++) {
 252       const int count0 = temporal_predictor_count[i][0];
 253       const int count1 = temporal_predictor_count[i][1];
 254
 255       t_nopred_prob[i] = get_binary_prob(count0, count1);
 256
 257       // Add in the predictor signaling cost
 258       t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +
 259                      count1 * vp9_cost_one(t_nopred_prob[i]);
 260     }
 261   }
 262
 263   // Now choose which coding method to use.
 264   if (t_pred_cost < no_pred_cost) {
 265     seg->temporal_update = 1;
 266     memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree));
 267     memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
 268   } else {
 269     seg->temporal_update = 0;
 270     memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree));
 271   }
 272 }
 273
 274 void vp9_reset_segment_features(struct segmentation *seg) {
 275   // Set up default state for MB feature flags
 276   seg->enabled = 0;
 277   seg->update_map = 0;
 278   seg->update_data = 0;
 279   memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
 280   vp9_clearall_segfeatures(seg);
 281 }