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Side by Side Diff: source/libvpx/vp9/encoder/vp9_segmentation.c

Issue 1124333011: libvpx: Pull from upstream (Closed) Base URL: https://chromium.googlesource.com/chromium/deps/libvpx.git@master
Patch Set: only update to last nights LKGR Created 5 years, 7 months ago
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1 /* 1 /*
2 * Copyright (c) 2012 The WebM project authors. All Rights Reserved. 2 * Copyright (c) 2012 The WebM project authors. All Rights Reserved.
3 * 3 *
4 * Use of this source code is governed by a BSD-style license 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 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 6 * tree. An additional intellectual property rights grant can be found
7 * in the file PATENTS. All contributing project authors may 7 * in the file PATENTS. All contributing project authors may
8 * be found in the AUTHORS file in the root of the source tree. 8 * be found in the AUTHORS file in the root of the source tree.
9 */ 9 */
10 10
(...skipping 18 matching lines...) Expand all
29 seg->enabled = 0; 29 seg->enabled = 0;
30 seg->update_map = 0; 30 seg->update_map = 0;
31 seg->update_data = 0; 31 seg->update_data = 0;
32 } 32 }
33 33
34 void vp9_set_segment_data(struct segmentation *seg, 34 void vp9_set_segment_data(struct segmentation *seg,
35 signed char *feature_data, 35 signed char *feature_data,
36 unsigned char abs_delta) { 36 unsigned char abs_delta) {
37 seg->abs_delta = abs_delta; 37 seg->abs_delta = abs_delta;
38 38
39 vpx_memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data)); 39 memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));
40 } 40 }
41 void vp9_disable_segfeature(struct segmentation *seg, int segment_id, 41 void vp9_disable_segfeature(struct segmentation *seg, int segment_id,
42 SEG_LVL_FEATURES feature_id) { 42 SEG_LVL_FEATURES feature_id) {
43 seg->feature_mask[segment_id] &= ~(1 << feature_id); 43 seg->feature_mask[segment_id] &= ~(1 << feature_id);
44 } 44 }
45 45
46 void vp9_clear_segdata(struct segmentation *seg, int segment_id, 46 void vp9_clear_segdata(struct segmentation *seg, int segment_id,
47 SEG_LVL_FEATURES feature_id) { 47 SEG_LVL_FEATURES feature_id) {
48 seg->feature_data[segment_id][feature_id] = 0; 48 seg->feature_data[segment_id][feature_id] = 0;
49 } 49 }
(...skipping 50 matching lines...) Expand 10 before | Expand all | Expand 10 after
100 segcounts[5] * vp9_cost_one(probs[5]); 100 segcounts[5] * vp9_cost_one(probs[5]);
101 if (c67 > 0) 101 if (c67 > 0)
102 cost += segcounts[6] * vp9_cost_zero(probs[6]) + 102 cost += segcounts[6] * vp9_cost_zero(probs[6]) +
103 segcounts[7] * vp9_cost_one(probs[6]); 103 segcounts[7] * vp9_cost_one(probs[6]);
104 } 104 }
105 105
106 return cost; 106 return cost;
107 } 107 }
108 108
109 static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd, 109 static void count_segs(const VP9_COMMON *cm, MACROBLOCKD *xd,
110 const TileInfo *tile, MODE_INFO *mi, 110 const TileInfo *tile, MODE_INFO **mi,
111 int *no_pred_segcounts, 111 int *no_pred_segcounts,
112 int (*temporal_predictor_count)[2], 112 int (*temporal_predictor_count)[2],
113 int *t_unpred_seg_counts, 113 int *t_unpred_seg_counts,
114 int bw, int bh, int mi_row, int mi_col) { 114 int bw, int bh, int mi_row, int mi_col) {
115 int segment_id; 115 int segment_id;
116 116
117 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) 117 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
118 return; 118 return;
119 119
120 xd->mi = mi; 120 xd->mi = mi;
121 segment_id = xd->mi[0].src_mi->mbmi.segment_id; 121 segment_id = xd->mi[0]->mbmi.segment_id;
122 122
123 set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols); 123 set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols);
124 124
125 // Count the number of hits on each segment with no prediction 125 // Count the number of hits on each segment with no prediction
126 no_pred_segcounts[segment_id]++; 126 no_pred_segcounts[segment_id]++;
127 127
128 // Temporal prediction not allowed on key frames 128 // Temporal prediction not allowed on key frames
129 if (cm->frame_type != KEY_FRAME) { 129 if (cm->frame_type != KEY_FRAME) {
130 const BLOCK_SIZE bsize = xd->mi[0].src_mi->mbmi.sb_type; 130 const BLOCK_SIZE bsize = xd->mi[0]->mbmi.sb_type;
131 // Test to see if the segment id matches the predicted value. 131 // Test to see if the segment id matches the predicted value.
132 const int pred_segment_id = vp9_get_segment_id(cm, cm->last_frame_seg_map, 132 const int pred_segment_id = vp9_get_segment_id(cm, cm->last_frame_seg_map,
133 bsize, mi_row, mi_col); 133 bsize, mi_row, mi_col);
134 const int pred_flag = pred_segment_id == segment_id; 134 const int pred_flag = pred_segment_id == segment_id;
135 const int pred_context = vp9_get_pred_context_seg_id(xd); 135 const int pred_context = vp9_get_pred_context_seg_id(xd);
136 136
137 // Store the prediction status for this mb and update counts 137 // Store the prediction status for this mb and update counts
138 // as appropriate 138 // as appropriate
139 xd->mi[0].src_mi->mbmi.seg_id_predicted = pred_flag; 139 xd->mi[0]->mbmi.seg_id_predicted = pred_flag;
140 temporal_predictor_count[pred_context][pred_flag]++; 140 temporal_predictor_count[pred_context][pred_flag]++;
141 141
142 // Update the "unpredicted" segment count 142 // Update the "unpredicted" segment count
143 if (!pred_flag) 143 if (!pred_flag)
144 t_unpred_seg_counts[segment_id]++; 144 t_unpred_seg_counts[segment_id]++;
145 } 145 }
146 } 146 }
147 147
148 static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd, 148 static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd,
149 const TileInfo *tile, MODE_INFO *mi, 149 const TileInfo *tile, MODE_INFO **mi,
150 int *no_pred_segcounts, 150 int *no_pred_segcounts,
151 int (*temporal_predictor_count)[2], 151 int (*temporal_predictor_count)[2],
152 int *t_unpred_seg_counts, 152 int *t_unpred_seg_counts,
153 int mi_row, int mi_col, 153 int mi_row, int mi_col,
154 BLOCK_SIZE bsize) { 154 BLOCK_SIZE bsize) {
155 const int mis = cm->mi_stride; 155 const int mis = cm->mi_stride;
156 int bw, bh; 156 int bw, bh;
157 const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2; 157 const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2;
158 158
159 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) 159 if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
160 return; 160 return;
161 161
162 bw = num_8x8_blocks_wide_lookup[mi[0].src_mi->mbmi.sb_type]; 162 bw = num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type];
163 bh = num_8x8_blocks_high_lookup[mi[0].src_mi->mbmi.sb_type]; 163 bh = num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type];
164 164
165 if (bw == bs && bh == bs) { 165 if (bw == bs && bh == bs) {
166 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, 166 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
167 t_unpred_seg_counts, bs, bs, mi_row, mi_col); 167 t_unpred_seg_counts, bs, bs, mi_row, mi_col);
168 } else if (bw == bs && bh < bs) { 168 } else if (bw == bs && bh < bs) {
169 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, 169 count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
170 t_unpred_seg_counts, bs, hbs, mi_row, mi_col); 170 t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
171 count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, 171 count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
172 temporal_predictor_count, t_unpred_seg_counts, bs, hbs, 172 temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
173 mi_row + hbs, mi_col); 173 mi_row + hbs, mi_col);
(...skipping 32 matching lines...) Expand 10 before | Expand all | Expand 10 after
206 int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } }; 206 int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };
207 int no_pred_segcounts[MAX_SEGMENTS] = { 0 }; 207 int no_pred_segcounts[MAX_SEGMENTS] = { 0 };
208 int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 }; 208 int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
209 209
210 vp9_prob no_pred_tree[SEG_TREE_PROBS]; 210 vp9_prob no_pred_tree[SEG_TREE_PROBS];
211 vp9_prob t_pred_tree[SEG_TREE_PROBS]; 211 vp9_prob t_pred_tree[SEG_TREE_PROBS];
212 vp9_prob t_nopred_prob[PREDICTION_PROBS]; 212 vp9_prob t_nopred_prob[PREDICTION_PROBS];
213 213
214 // Set default state for the segment tree probabilities and the 214 // Set default state for the segment tree probabilities and the
215 // temporal coding probabilities 215 // temporal coding probabilities
216 vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 216 memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
217 vpx_memset(seg->pred_probs, 255, sizeof(seg->pred_probs)); 217 memset(seg->pred_probs, 255, sizeof(seg->pred_probs));
218 218
219 // First of all generate stats regarding how well the last segment map 219 // First of all generate stats regarding how well the last segment map
220 // predicts this one 220 // predicts this one
221 for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) { 221 for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {
222 TileInfo tile; 222 TileInfo tile;
223 MODE_INFO *mi_ptr; 223 MODE_INFO **mi_ptr;
224 vp9_tile_init(&tile, cm, 0, tile_col); 224 vp9_tile_init(&tile, cm, 0, tile_col);
225 225
226 mi_ptr = cm->mi + tile.mi_col_start; 226 mi_ptr = cm->mi_grid_visible + tile.mi_col_start;
227 for (mi_row = 0; mi_row < cm->mi_rows; 227 for (mi_row = 0; mi_row < cm->mi_rows;
228 mi_row += 8, mi_ptr += 8 * cm->mi_stride) { 228 mi_row += 8, mi_ptr += 8 * cm->mi_stride) {
229 MODE_INFO *mi = mi_ptr; 229 MODE_INFO **mi = mi_ptr;
230 for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end; 230 for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;
231 mi_col += 8, mi += 8) 231 mi_col += 8, mi += 8)
232 count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts, 232 count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,
233 temporal_predictor_count, t_unpred_seg_counts, 233 temporal_predictor_count, t_unpred_seg_counts,
234 mi_row, mi_col, BLOCK_64X64); 234 mi_row, mi_col, BLOCK_64X64);
235 } 235 }
236 } 236 }
237 237
238 // Work out probability tree for coding segments without prediction 238 // Work out probability tree for coding segments without prediction
239 // and the cost. 239 // and the cost.
(...skipping 16 matching lines...) Expand all
256 256
257 // Add in the predictor signaling cost 257 // Add in the predictor signaling cost
258 t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) + 258 t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +
259 count1 * vp9_cost_one(t_nopred_prob[i]); 259 count1 * vp9_cost_one(t_nopred_prob[i]);
260 } 260 }
261 } 261 }
262 262
263 // Now choose which coding method to use. 263 // Now choose which coding method to use.
264 if (t_pred_cost < no_pred_cost) { 264 if (t_pred_cost < no_pred_cost) {
265 seg->temporal_update = 1; 265 seg->temporal_update = 1;
266 vpx_memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree)); 266 memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree));
267 vpx_memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob)); 267 memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
268 } else { 268 } else {
269 seg->temporal_update = 0; 269 seg->temporal_update = 0;
270 vpx_memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree)); 270 memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree));
271 } 271 }
272 } 272 }
273 273
274 void vp9_reset_segment_features(struct segmentation *seg) { 274 void vp9_reset_segment_features(struct segmentation *seg) {
275 // Set up default state for MB feature flags 275 // Set up default state for MB feature flags
276 seg->enabled = 0; 276 seg->enabled = 0;
277 seg->update_map = 0; 277 seg->update_map = 0;
278 seg->update_data = 0; 278 seg->update_data = 0;
279 vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); 279 memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
280 vp9_clearall_segfeatures(seg); 280 vp9_clearall_segfeatures(seg);
281 } 281 }
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