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 vpx_memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data)); 40 41 // TBD ?? Set the feature mask 42 // vpx_memcpy(cpi->mb.e_mbd.segment_feature_mask, 0, 43 // sizeof(cpi->mb.e_mbd.segment_feature_mask)); 44} 45void vp9_disable_segfeature(struct segmentation *seg, int segment_id, 46 SEG_LVL_FEATURES feature_id) { 47 seg->feature_mask[segment_id] &= ~(1 << feature_id); 48} 49 50void vp9_clear_segdata(struct segmentation *seg, int segment_id, 51 SEG_LVL_FEATURES feature_id) { 52 seg->feature_data[segment_id][feature_id] = 0; 53} 54 55// Based on set of segment counts calculate a probability tree 56static void calc_segtree_probs(int *segcounts, vp9_prob *segment_tree_probs) { 57 // Work out probabilities of each segment 58 const int c01 = segcounts[0] + segcounts[1]; 59 const int c23 = segcounts[2] + segcounts[3]; 60 const int c45 = segcounts[4] + segcounts[5]; 61 const int c67 = segcounts[6] + segcounts[7]; 62 63 segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67); 64 segment_tree_probs[1] = get_binary_prob(c01, c23); 65 segment_tree_probs[2] = get_binary_prob(c45, c67); 66 segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]); 67 segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]); 68 segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]); 69 segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]); 70} 71 72// Based on set of segment counts and probabilities calculate a cost estimate 73static int cost_segmap(int *segcounts, vp9_prob *probs) { 74 const int c01 = segcounts[0] + segcounts[1]; 75 const int c23 = segcounts[2] + segcounts[3]; 76 const int c45 = segcounts[4] + segcounts[5]; 77 const int c67 = segcounts[6] + segcounts[7]; 78 const int c0123 = c01 + c23; 79 const int c4567 = c45 + c67; 80 81 // Cost the top node of the tree 82 int cost = c0123 * vp9_cost_zero(probs[0]) + 83 c4567 * vp9_cost_one(probs[0]); 84 85 // Cost subsequent levels 86 if (c0123 > 0) { 87 cost += c01 * vp9_cost_zero(probs[1]) + 88 c23 * vp9_cost_one(probs[1]); 89 90 if (c01 > 0) 91 cost += segcounts[0] * vp9_cost_zero(probs[3]) + 92 segcounts[1] * vp9_cost_one(probs[3]); 93 if (c23 > 0) 94 cost += segcounts[2] * vp9_cost_zero(probs[4]) + 95 segcounts[3] * vp9_cost_one(probs[4]); 96 } 97 98 if (c4567 > 0) { 99 cost += c45 * vp9_cost_zero(probs[2]) + 100 c67 * vp9_cost_one(probs[2]); 101 102 if (c45 > 0) 103 cost += segcounts[4] * vp9_cost_zero(probs[5]) + 104 segcounts[5] * vp9_cost_one(probs[5]); 105 if (c67 > 0) 106 cost += segcounts[6] * vp9_cost_zero(probs[6]) + 107 segcounts[7] * vp9_cost_one(probs[6]); 108 } 109 110 return cost; 111} 112 113static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd, 114 const TileInfo *tile, MODE_INFO *mi, 115 int *no_pred_segcounts, 116 int (*temporal_predictor_count)[2], 117 int *t_unpred_seg_counts, 118 int bw, int bh, int mi_row, int mi_col) { 119 int segment_id; 120 121 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) 122 return; 123 124 xd->mi = mi; 125 segment_id = xd->mi[0].src_mi->mbmi.segment_id; 126 127 set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols); 128 129 // Count the number of hits on each segment with no prediction 130 no_pred_segcounts[segment_id]++; 131 132 // Temporal prediction not allowed on key frames 133 if (cm->frame_type != KEY_FRAME) { 134 const BLOCK_SIZE bsize = xd->mi[0].src_mi->mbmi.sb_type; 135 // Test to see if the segment id matches the predicted value. 136 const int pred_segment_id = vp9_get_segment_id(cm, cm->last_frame_seg_map, 137 bsize, mi_row, mi_col); 138 const int pred_flag = pred_segment_id == segment_id; 139 const int pred_context = vp9_get_pred_context_seg_id(xd); 140 141 // Store the prediction status for this mb and update counts 142 // as appropriate 143 xd->mi[0].src_mi->mbmi.seg_id_predicted = pred_flag; 144 temporal_predictor_count[pred_context][pred_flag]++; 145 146 // Update the "unpredicted" segment count 147 if (!pred_flag) 148 t_unpred_seg_counts[segment_id]++; 149 } 150} 151 152static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd, 153 const TileInfo *tile, MODE_INFO *mi, 154 int *no_pred_segcounts, 155 int (*temporal_predictor_count)[2], 156 int *t_unpred_seg_counts, 157 int mi_row, int mi_col, 158 BLOCK_SIZE bsize) { 159 const int mis = cm->mi_stride; 160 int bw, bh; 161 const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2; 162 163 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) 164 return; 165 166 bw = num_8x8_blocks_wide_lookup[mi[0].src_mi->mbmi.sb_type]; 167 bh = num_8x8_blocks_high_lookup[mi[0].src_mi->mbmi.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, 182 no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, 183 hbs, bs, mi_row, 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], 195 no_pred_segcounts, temporal_predictor_count, 196 t_unpred_seg_counts, 197 mi_row + mi_dr, mi_col + mi_dc, subsize); 198 } 199 } 200} 201 202void vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) { 203 struct segmentation *seg = &cm->seg; 204 205 int no_pred_cost; 206 int t_pred_cost = INT_MAX; 207 208 int i, tile_col, mi_row, mi_col; 209 210 int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } }; 211 int no_pred_segcounts[MAX_SEGMENTS] = { 0 }; 212 int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 }; 213 214 vp9_prob no_pred_tree[SEG_TREE_PROBS]; 215 vp9_prob t_pred_tree[SEG_TREE_PROBS]; 216 vp9_prob t_nopred_prob[PREDICTION_PROBS]; 217 218 // Set default state for the segment tree probabilities and the 219 // temporal coding probabilities 220 vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 221 vpx_memset(seg->pred_probs, 255, sizeof(seg->pred_probs)); 222 223 // First of all generate stats regarding how well the last segment map 224 // predicts this one 225 for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) { 226 TileInfo tile; 227 MODE_INFO *mi_ptr; 228 vp9_tile_init(&tile, cm, 0, tile_col); 229 230 mi_ptr = cm->mi + tile.mi_col_start; 231 for (mi_row = 0; mi_row < cm->mi_rows; 232 mi_row += 8, mi_ptr += 8 * cm->mi_stride) { 233 MODE_INFO *mi = mi_ptr; 234 for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end; 235 mi_col += 8, mi += 8) 236 count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts, 237 temporal_predictor_count, t_unpred_seg_counts, 238 mi_row, mi_col, BLOCK_64X64); 239 } 240 } 241 242 // Work out probability tree for coding segments without prediction 243 // and the cost. 244 calc_segtree_probs(no_pred_segcounts, no_pred_tree); 245 no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree); 246 247 // Key frames cannot use temporal prediction 248 if (!frame_is_intra_only(cm)) { 249 // Work out probability tree for coding those segments not 250 // predicted using the temporal method and the cost. 251 calc_segtree_probs(t_unpred_seg_counts, t_pred_tree); 252 t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree); 253 254 // Add in the cost of the signaling for each prediction context. 255 for (i = 0; i < PREDICTION_PROBS; i++) { 256 const int count0 = temporal_predictor_count[i][0]; 257 const int count1 = temporal_predictor_count[i][1]; 258 259 t_nopred_prob[i] = get_binary_prob(count0, count1); 260 261 // Add in the predictor signaling cost 262 t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) + 263 count1 * vp9_cost_one(t_nopred_prob[i]); 264 } 265 } 266 267 // Now choose which coding method to use. 268 if (t_pred_cost < no_pred_cost) { 269 seg->temporal_update = 1; 270 vpx_memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree)); 271 vpx_memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob)); 272 } else { 273 seg->temporal_update = 0; 274 vpx_memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree)); 275 } 276} 277 278void vp9_reset_segment_features(struct segmentation *seg) { 279 // Set up default state for MB feature flags 280 seg->enabled = 0; 281 seg->update_map = 0; 282 seg->update_data = 0; 283 vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 284 vp9_clearall_segfeatures(seg); 285} 286