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 #include "vpx_mem/vpx_mem.h" 14 #include "vp9/encoder/vp9_segmentation.h" 15 #include "vp9/common/vp9_pred_common.h" 16 #include "vp9/common/vp9_tile_common.h" 17 18 void vp9_enable_segmentation(VP9_PTR ptr) { 19 VP9_COMP *cpi = (VP9_COMP *)ptr; 20 struct segmentation *const seg = &cpi->common.seg; 21 22 seg->enabled = 1; 23 seg->update_map = 1; 24 seg->update_data = 1; 25 } 26 27 void vp9_disable_segmentation(VP9_PTR ptr) { 28 VP9_COMP *cpi = (VP9_COMP *)ptr; 29 struct segmentation *const seg = &cpi->common.seg; 30 seg->enabled = 0; 31 } 32 33 void vp9_set_segmentation_map(VP9_PTR ptr, 34 unsigned char *segmentation_map) { 35 VP9_COMP *cpi = (VP9_COMP *)ptr; 36 struct segmentation *const seg = &cpi->common.seg; 37 38 // Copy in the new segmentation map 39 vpx_memcpy(cpi->segmentation_map, segmentation_map, 40 (cpi->common.mi_rows * cpi->common.mi_cols)); 41 42 // Signal that the map should be updated. 43 seg->update_map = 1; 44 seg->update_data = 1; 45 } 46 47 void vp9_set_segment_data(VP9_PTR ptr, 48 signed char *feature_data, 49 unsigned char abs_delta) { 50 VP9_COMP *cpi = (VP9_COMP *)ptr; 51 struct segmentation *const seg = &cpi->common.seg; 52 53 seg->abs_delta = abs_delta; 54 55 vpx_memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data)); 56 57 // TBD ?? Set the feature mask 58 // vpx_memcpy(cpi->mb.e_mbd.segment_feature_mask, 0, 59 // sizeof(cpi->mb.e_mbd.segment_feature_mask)); 60 } 61 62 // Based on set of segment counts calculate a probability tree 63 static void calc_segtree_probs(int *segcounts, vp9_prob *segment_tree_probs) { 64 // Work out probabilities of each segment 65 const int c01 = segcounts[0] + segcounts[1]; 66 const int c23 = segcounts[2] + segcounts[3]; 67 const int c45 = segcounts[4] + segcounts[5]; 68 const int c67 = segcounts[6] + segcounts[7]; 69 70 segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67); 71 segment_tree_probs[1] = get_binary_prob(c01, c23); 72 segment_tree_probs[2] = get_binary_prob(c45, c67); 73 segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]); 74 segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]); 75 segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]); 76 segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]); 77 } 78 79 // Based on set of segment counts and probabilities calculate a cost estimate 80 static int cost_segmap(int *segcounts, vp9_prob *probs) { 81 const int c01 = segcounts[0] + segcounts[1]; 82 const int c23 = segcounts[2] + segcounts[3]; 83 const int c45 = segcounts[4] + segcounts[5]; 84 const int c67 = segcounts[6] + segcounts[7]; 85 const int c0123 = c01 + c23; 86 const int c4567 = c45 + c67; 87 88 // Cost the top node of the tree 89 int cost = c0123 * vp9_cost_zero(probs[0]) + 90 c4567 * vp9_cost_one(probs[0]); 91 92 // Cost subsequent levels 93 if (c0123 > 0) { 94 cost += c01 * vp9_cost_zero(probs[1]) + 95 c23 * vp9_cost_one(probs[1]); 96 97 if (c01 > 0) 98 cost += segcounts[0] * vp9_cost_zero(probs[3]) + 99 segcounts[1] * vp9_cost_one(probs[3]); 100 if (c23 > 0) 101 cost += segcounts[2] * vp9_cost_zero(probs[4]) + 102 segcounts[3] * vp9_cost_one(probs[4]); 103 } 104 105 if (c4567 > 0) { 106 cost += c45 * vp9_cost_zero(probs[2]) + 107 c67 * vp9_cost_one(probs[2]); 108 109 if (c45 > 0) 110 cost += segcounts[4] * vp9_cost_zero(probs[5]) + 111 segcounts[5] * vp9_cost_one(probs[5]); 112 if (c67 > 0) 113 cost += segcounts[6] * vp9_cost_zero(probs[6]) + 114 segcounts[7] * vp9_cost_one(probs[6]); 115 } 116 117 return cost; 118 } 119 120 static void count_segs(VP9_COMP *cpi, const TileInfo *const tile, 121 MODE_INFO **mi_8x8, 122 int *no_pred_segcounts, 123 int (*temporal_predictor_count)[2], 124 int *t_unpred_seg_counts, 125 int bw, int bh, int mi_row, int mi_col) { 126 VP9_COMMON *const cm = &cpi->common; 127 MACROBLOCKD *const xd = &cpi->mb.e_mbd; 128 int segment_id; 129 130 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) 131 return; 132 133 xd->mi_8x8 = mi_8x8; 134 segment_id = xd->mi_8x8[0]->mbmi.segment_id; 135 136 set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols); 137 138 // Count the number of hits on each segment with no prediction 139 no_pred_segcounts[segment_id]++; 140 141 // Temporal prediction not allowed on key frames 142 if (cm->frame_type != KEY_FRAME) { 143 const BLOCK_SIZE bsize = mi_8x8[0]->mbmi.sb_type; 144 // Test to see if the segment id matches the predicted value. 145 const int pred_segment_id = vp9_get_segment_id(cm, cm->last_frame_seg_map, 146 bsize, mi_row, mi_col); 147 const int pred_flag = pred_segment_id == segment_id; 148 const int pred_context = vp9_get_pred_context_seg_id(xd); 149 150 // Store the prediction status for this mb and update counts 151 // as appropriate 152 vp9_set_pred_flag_seg_id(xd, pred_flag); 153 temporal_predictor_count[pred_context][pred_flag]++; 154 155 if (!pred_flag) 156 // Update the "unpredicted" segment count 157 t_unpred_seg_counts[segment_id]++; 158 } 159 } 160 161 static void count_segs_sb(VP9_COMP *cpi, const TileInfo *const tile, 162 MODE_INFO **mi_8x8, 163 int *no_pred_segcounts, 164 int (*temporal_predictor_count)[2], 165 int *t_unpred_seg_counts, 166 int mi_row, int mi_col, 167 BLOCK_SIZE bsize) { 168 const VP9_COMMON *const cm = &cpi->common; 169 const int mis = cm->mode_info_stride; 170 int bw, bh; 171 const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2; 172 173 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) 174 return; 175 176 bw = num_8x8_blocks_wide_lookup[mi_8x8[0]->mbmi.sb_type]; 177 bh = num_8x8_blocks_high_lookup[mi_8x8[0]->mbmi.sb_type]; 178 179 if (bw == bs && bh == bs) { 180 count_segs(cpi, tile, mi_8x8, no_pred_segcounts, temporal_predictor_count, 181 t_unpred_seg_counts, bs, bs, mi_row, mi_col); 182 } else if (bw == bs && bh < bs) { 183 count_segs(cpi, tile, mi_8x8, no_pred_segcounts, temporal_predictor_count, 184 t_unpred_seg_counts, bs, hbs, mi_row, mi_col); 185 count_segs(cpi, tile, mi_8x8 + hbs * mis, no_pred_segcounts, 186 temporal_predictor_count, t_unpred_seg_counts, bs, hbs, 187 mi_row + hbs, mi_col); 188 } else if (bw < bs && bh == bs) { 189 count_segs(cpi, tile, mi_8x8, no_pred_segcounts, temporal_predictor_count, 190 t_unpred_seg_counts, hbs, bs, mi_row, mi_col); 191 count_segs(cpi, tile, mi_8x8 + hbs, 192 no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, 193 hbs, bs, mi_row, mi_col + hbs); 194 } else { 195 const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize]; 196 int n; 197 198 assert(bw < bs && bh < bs); 199 200 for (n = 0; n < 4; n++) { 201 const int mi_dc = hbs * (n & 1); 202 const int mi_dr = hbs * (n >> 1); 203 204 count_segs_sb(cpi, tile, &mi_8x8[mi_dr * mis + mi_dc], 205 no_pred_segcounts, temporal_predictor_count, 206 t_unpred_seg_counts, 207 mi_row + mi_dr, mi_col + mi_dc, subsize); 208 } 209 } 210 } 211 212 void vp9_choose_segmap_coding_method(VP9_COMP *cpi) { 213 VP9_COMMON *const cm = &cpi->common; 214 struct segmentation *seg = &cm->seg; 215 216 int no_pred_cost; 217 int t_pred_cost = INT_MAX; 218 219 int i, tile_col, mi_row, mi_col; 220 221 int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } }; 222 int no_pred_segcounts[MAX_SEGMENTS] = { 0 }; 223 int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 }; 224 225 vp9_prob no_pred_tree[SEG_TREE_PROBS]; 226 vp9_prob t_pred_tree[SEG_TREE_PROBS]; 227 vp9_prob t_nopred_prob[PREDICTION_PROBS]; 228 229 const int mis = cm->mode_info_stride; 230 MODE_INFO **mi_ptr, **mi; 231 232 // Set default state for the segment tree probabilities and the 233 // temporal coding probabilities 234 vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 235 vpx_memset(seg->pred_probs, 255, sizeof(seg->pred_probs)); 236 237 // First of all generate stats regarding how well the last segment map 238 // predicts this one 239 for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) { 240 TileInfo tile; 241 242 vp9_tile_init(&tile, cm, 0, tile_col); 243 mi_ptr = cm->mi_grid_visible + tile.mi_col_start; 244 for (mi_row = 0; mi_row < cm->mi_rows; 245 mi_row += 8, mi_ptr += 8 * mis) { 246 mi = mi_ptr; 247 for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end; 248 mi_col += 8, mi += 8) 249 count_segs_sb(cpi, &tile, mi, no_pred_segcounts, 250 temporal_predictor_count, t_unpred_seg_counts, 251 mi_row, mi_col, BLOCK_64X64); 252 } 253 } 254 255 // Work out probability tree for coding segments without prediction 256 // and the cost. 257 calc_segtree_probs(no_pred_segcounts, no_pred_tree); 258 no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree); 259 260 // Key frames cannot use temporal prediction 261 if (!frame_is_intra_only(cm)) { 262 // Work out probability tree for coding those segments not 263 // predicted using the temporal method and the cost. 264 calc_segtree_probs(t_unpred_seg_counts, t_pred_tree); 265 t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree); 266 267 // Add in the cost of the signaling for each prediction context. 268 for (i = 0; i < PREDICTION_PROBS; i++) { 269 const int count0 = temporal_predictor_count[i][0]; 270 const int count1 = temporal_predictor_count[i][1]; 271 272 t_nopred_prob[i] = get_binary_prob(count0, count1); 273 274 // Add in the predictor signaling cost 275 t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) + 276 count1 * vp9_cost_one(t_nopred_prob[i]); 277 } 278 } 279 280 // Now choose which coding method to use. 281 if (t_pred_cost < no_pred_cost) { 282 seg->temporal_update = 1; 283 vpx_memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree)); 284 vpx_memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob)); 285 } else { 286 seg->temporal_update = 0; 287 vpx_memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree)); 288 } 289 } 290
