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 22void vp9_enable_segmentation(struct segmentation *seg) { 23 seg->enabled = 1; 24 seg->update_map = 1; 25 seg->update_data = 1; 26} 27 28void vp9_disable_segmentation(struct segmentation *seg) { 29 seg->enabled = 0; 30 seg->update_map = 0; 31 seg->update_data = 0; 32} 33 34void 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} 41void 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 46void 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 52static void calc_segtree_probs(int *segcounts, vpx_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 69static int cost_segmap(int *segcounts, vpx_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 109static 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 148static 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 198void 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 vpx_prob no_pred_tree[SEG_TREE_PROBS]; 211 vpx_prob t_pred_tree[SEG_TREE_PROBS]; 212 vpx_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 274void 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} 282