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 void vp9_psnr_aq_mode_setup(struct segmentation *seg) { 50 int i; 51 52 vp9_enable_segmentation(seg); 53 vp9_clearall_segfeatures(seg); 54 seg->abs_delta = SEGMENT_DELTADATA; 55 56 for (i = 0; i < MAX_SEGMENTS; ++i) { 57 vp9_set_segdata(seg, i, SEG_LVL_ALT_Q, 2 * (i - (MAX_SEGMENTS / 2))); 58 vp9_enable_segfeature(seg, i, SEG_LVL_ALT_Q); 59 } 60 } 61 62 // Based on set of segment counts calculate a probability tree 63 static void calc_segtree_probs(int *segcounts, vpx_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, vpx_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]) + c4567 * vp9_cost_one(probs[0]); 90 91 // Cost subsequent levels 92 if (c0123 > 0) { 93 cost += c01 * vp9_cost_zero(probs[1]) + c23 * vp9_cost_one(probs[1]); 94 95 if (c01 > 0) 96 cost += segcounts[0] * vp9_cost_zero(probs[3]) + 97 segcounts[1] * vp9_cost_one(probs[3]); 98 if (c23 > 0) 99 cost += segcounts[2] * vp9_cost_zero(probs[4]) + 100 segcounts[3] * vp9_cost_one(probs[4]); 101 } 102 103 if (c4567 > 0) { 104 cost += c45 * vp9_cost_zero(probs[2]) + c67 * vp9_cost_one(probs[2]); 105 106 if (c45 > 0) 107 cost += segcounts[4] * vp9_cost_zero(probs[5]) + 108 segcounts[5] * vp9_cost_one(probs[5]); 109 if (c67 > 0) 110 cost += segcounts[6] * vp9_cost_zero(probs[6]) + 111 segcounts[7] * vp9_cost_one(probs[6]); 112 } 113 114 return cost; 115 } 116 117 static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd, 118 const TileInfo *tile, MODE_INFO **mi, 119 int *no_pred_segcounts, 120 int (*temporal_predictor_count)[2], 121 int *t_unpred_seg_counts, int bw, int bh, int mi_row, 122 int mi_col) { 123 int segment_id; 124 125 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; 126 127 xd->mi = mi; 128 segment_id = xd->mi[0]->segment_id; 129 130 set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols); 131 132 // Count the number of hits on each segment with no prediction 133 no_pred_segcounts[segment_id]++; 134 135 // Temporal prediction not allowed on key frames 136 if (cm->frame_type != KEY_FRAME) { 137 const BLOCK_SIZE bsize = xd->mi[0]->sb_type; 138 // Test to see if the segment id matches the predicted value. 139 const int pred_segment_id = 140 get_segment_id(cm, cm->last_frame_seg_map, bsize, mi_row, mi_col); 141 const int pred_flag = pred_segment_id == segment_id; 142 const int pred_context = vp9_get_pred_context_seg_id(xd); 143 144 // Store the prediction status for this mb and update counts 145 // as appropriate 146 xd->mi[0]->seg_id_predicted = pred_flag; 147 temporal_predictor_count[pred_context][pred_flag]++; 148 149 // Update the "unpredicted" segment count 150 if (!pred_flag) t_unpred_seg_counts[segment_id]++; 151 } 152 } 153 154 static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd, 155 const TileInfo *tile, MODE_INFO **mi, 156 int *no_pred_segcounts, 157 int (*temporal_predictor_count)[2], 158 int *t_unpred_seg_counts, int mi_row, int mi_col, 159 BLOCK_SIZE bsize) { 160 const int mis = cm->mi_stride; 161 int bw, bh; 162 const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2; 163 164 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; 165 166 bw = num_8x8_blocks_wide_lookup[mi[0]->sb_type]; 167 bh = num_8x8_blocks_high_lookup[mi[0]->sb_type]; 168 169 if (bw == bs && bh == bs) { 170 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, 171 t_unpred_seg_counts, bs, bs, mi_row, mi_col); 172 } else if (bw == bs && bh < bs) { 173 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, 174 t_unpred_seg_counts, bs, hbs, mi_row, mi_col); 175 count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, 176 temporal_predictor_count, t_unpred_seg_counts, bs, hbs, 177 mi_row + hbs, mi_col); 178 } else if (bw < bs && bh == bs) { 179 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, 180 t_unpred_seg_counts, hbs, bs, mi_row, mi_col); 181 count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts, 182 temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row, 183 mi_col + hbs); 184 } else { 185 const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize]; 186 int n; 187 188 assert(bw < bs && bh < bs); 189 190 for (n = 0; n < 4; n++) { 191 const int mi_dc = hbs * (n & 1); 192 const int mi_dr = hbs * (n >> 1); 193 194 count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts, 195 temporal_predictor_count, t_unpred_seg_counts, 196 mi_row + mi_dr, mi_col + mi_dc, subsize); 197 } 198 } 199 } 200 201 void vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) { 202 struct segmentation *seg = &cm->seg; 203 204 int no_pred_cost; 205 int t_pred_cost = INT_MAX; 206 207 int i, tile_col, mi_row, mi_col; 208 209 int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } }; 210 int no_pred_segcounts[MAX_SEGMENTS] = { 0 }; 211 int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 }; 212 213 vpx_prob no_pred_tree[SEG_TREE_PROBS]; 214 vpx_prob t_pred_tree[SEG_TREE_PROBS]; 215 vpx_prob t_nopred_prob[PREDICTION_PROBS]; 216 217 // Set default state for the segment tree probabilities and the 218 // temporal coding probabilities 219 memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 220 memset(seg->pred_probs, 255, sizeof(seg->pred_probs)); 221 222 // First of all generate stats regarding how well the last segment map 223 // predicts this one 224 for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) { 225 TileInfo tile; 226 MODE_INFO **mi_ptr; 227 vp9_tile_init(&tile, cm, 0, tile_col); 228 229 mi_ptr = cm->mi_grid_visible + tile.mi_col_start; 230 for (mi_row = 0; mi_row < cm->mi_rows; 231 mi_row += 8, mi_ptr += 8 * cm->mi_stride) { 232 MODE_INFO **mi = mi_ptr; 233 for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end; 234 mi_col += 8, mi += 8) 235 count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts, 236 temporal_predictor_count, t_unpred_seg_counts, mi_row, 237 mi_col, BLOCK_64X64); 238 } 239 } 240 241 // Work out probability tree for coding segments without prediction 242 // and the cost. 243 calc_segtree_probs(no_pred_segcounts, no_pred_tree); 244 no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree); 245 246 // Key frames cannot use temporal prediction 247 if (!frame_is_intra_only(cm)) { 248 // Work out probability tree for coding those segments not 249 // predicted using the temporal method and the cost. 250 calc_segtree_probs(t_unpred_seg_counts, t_pred_tree); 251 t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree); 252 253 // Add in the cost of the signaling for each prediction context. 254 for (i = 0; i < PREDICTION_PROBS; i++) { 255 const int count0 = temporal_predictor_count[i][0]; 256 const int count1 = temporal_predictor_count[i][1]; 257 258 t_nopred_prob[i] = get_binary_prob(count0, count1); 259 260 // Add in the predictor signaling cost 261 t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) + 262 count1 * vp9_cost_one(t_nopred_prob[i]); 263 } 264 } 265 266 // Now choose which coding method to use. 267 if (t_pred_cost < no_pred_cost) { 268 seg->temporal_update = 1; 269 memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree)); 270 memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob)); 271 } else { 272 seg->temporal_update = 0; 273 memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree)); 274 } 275 } 276 277 void vp9_reset_segment_features(struct segmentation *seg) { 278 // Set up default state for MB feature flags 279 seg->enabled = 0; 280 seg->update_map = 0; 281 seg->update_data = 0; 282 memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 283 vp9_clearall_segfeatures(seg); 284 } 285
