Line data Source code
1 : /*
2 : * Copyright (c) 2016, Alliance for Open Media. All rights reserved
3 : *
4 : * This source code is subject to the terms of the BSD 2 Clause License and
5 : * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
6 : * was not distributed with this source code in the LICENSE file, you can
7 : * obtain it at www.aomedia.org/license/software. If the Alliance for Open
8 : * Media Patent License 1.0 was not distributed with this source code in the
9 : * PATENTS file, you can obtain it at www.aomedia.org/license/patent.
10 : */
11 :
12 : #include <limits.h>
13 :
14 : #include "aom_mem/aom_mem.h"
15 :
16 : #include "av1/common/pred_common.h"
17 : #include "av1/common/tile_common.h"
18 :
19 : #include "av1/encoder/cost.h"
20 : #include "av1/encoder/segmentation.h"
21 : #include "av1/encoder/subexp.h"
22 :
23 0 : void av1_enable_segmentation(struct segmentation *seg) {
24 0 : seg->enabled = 1;
25 0 : seg->update_map = 1;
26 0 : seg->update_data = 1;
27 0 : }
28 :
29 0 : void av1_disable_segmentation(struct segmentation *seg) {
30 0 : seg->enabled = 0;
31 0 : seg->update_map = 0;
32 0 : seg->update_data = 0;
33 0 : }
34 :
35 0 : void av1_set_segment_data(struct segmentation *seg, signed char *feature_data,
36 : unsigned char abs_delta) {
37 0 : seg->abs_delta = abs_delta;
38 :
39 0 : memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));
40 0 : }
41 0 : void av1_disable_segfeature(struct segmentation *seg, int segment_id,
42 : SEG_LVL_FEATURES feature_id) {
43 0 : seg->feature_mask[segment_id] &= ~(1 << feature_id);
44 0 : }
45 :
46 0 : void av1_clear_segdata(struct segmentation *seg, int segment_id,
47 : SEG_LVL_FEATURES feature_id) {
48 0 : seg->feature_data[segment_id][feature_id] = 0;
49 0 : }
50 :
51 : // Based on set of segment counts calculate a probability tree
52 0 : static void calc_segtree_probs(unsigned *segcounts,
53 : aom_prob *segment_tree_probs,
54 : const aom_prob *cur_tree_probs,
55 : const int probwt) {
56 : // Work out probabilities of each segment
57 0 : const unsigned cc[4] = { segcounts[0] + segcounts[1],
58 0 : segcounts[2] + segcounts[3],
59 0 : segcounts[4] + segcounts[5],
60 0 : segcounts[6] + segcounts[7] };
61 0 : const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] };
62 : int i;
63 :
64 0 : segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]);
65 0 : segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]);
66 0 : segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]);
67 0 : segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
68 0 : segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
69 0 : segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
70 0 : segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
71 :
72 0 : for (i = 0; i < 7; i++) {
73 0 : const unsigned *ct =
74 0 : i == 0 ? ccc : i < 3 ? cc + (i & 2) : segcounts + (i - 3) * 2;
75 0 : av1_prob_diff_update_savings_search(ct, cur_tree_probs[i],
76 : &segment_tree_probs[i],
77 : DIFF_UPDATE_PROB, probwt);
78 : }
79 0 : }
80 :
81 : // Based on set of segment counts and probabilities calculate a cost estimate
82 0 : static int cost_segmap(unsigned *segcounts, aom_prob *probs) {
83 0 : const int c01 = segcounts[0] + segcounts[1];
84 0 : const int c23 = segcounts[2] + segcounts[3];
85 0 : const int c45 = segcounts[4] + segcounts[5];
86 0 : const int c67 = segcounts[6] + segcounts[7];
87 0 : const int c0123 = c01 + c23;
88 0 : const int c4567 = c45 + c67;
89 :
90 : // Cost the top node of the tree
91 0 : int cost = c0123 * av1_cost_zero(probs[0]) + c4567 * av1_cost_one(probs[0]);
92 :
93 : // Cost subsequent levels
94 0 : if (c0123 > 0) {
95 0 : cost += c01 * av1_cost_zero(probs[1]) + c23 * av1_cost_one(probs[1]);
96 :
97 0 : if (c01 > 0)
98 0 : cost += segcounts[0] * av1_cost_zero(probs[3]) +
99 0 : segcounts[1] * av1_cost_one(probs[3]);
100 0 : if (c23 > 0)
101 0 : cost += segcounts[2] * av1_cost_zero(probs[4]) +
102 0 : segcounts[3] * av1_cost_one(probs[4]);
103 : }
104 :
105 0 : if (c4567 > 0) {
106 0 : cost += c45 * av1_cost_zero(probs[2]) + c67 * av1_cost_one(probs[2]);
107 :
108 0 : if (c45 > 0)
109 0 : cost += segcounts[4] * av1_cost_zero(probs[5]) +
110 0 : segcounts[5] * av1_cost_one(probs[5]);
111 0 : if (c67 > 0)
112 0 : cost += segcounts[6] * av1_cost_zero(probs[6]) +
113 0 : segcounts[7] * av1_cost_one(probs[6]);
114 : }
115 :
116 0 : return cost;
117 : }
118 :
119 0 : static void count_segs(const AV1_COMMON *cm, MACROBLOCKD *xd,
120 : const TileInfo *tile, MODE_INFO **mi,
121 : unsigned *no_pred_segcounts,
122 : unsigned (*temporal_predictor_count)[2],
123 : unsigned *t_unpred_seg_counts, int bw, int bh,
124 : int mi_row, int mi_col) {
125 : int segment_id;
126 :
127 0 : if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;
128 :
129 0 : xd->mi = mi;
130 0 : segment_id = xd->mi[0]->mbmi.segment_id;
131 :
132 0 : set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw,
133 : #if CONFIG_DEPENDENT_HORZTILES
134 : cm->dependent_horz_tiles,
135 : #endif // CONFIG_DEPENDENT_HORZTILES
136 : cm->mi_rows, cm->mi_cols);
137 :
138 : // Count the number of hits on each segment with no prediction
139 0 : no_pred_segcounts[segment_id]++;
140 :
141 : // Temporal prediction not allowed on key frames
142 0 : if (cm->frame_type != KEY_FRAME) {
143 0 : const BLOCK_SIZE bsize = xd->mi[0]->mbmi.sb_type;
144 : // Test to see if the segment id matches the predicted value.
145 0 : const int pred_segment_id =
146 0 : get_segment_id(cm, cm->last_frame_seg_map, bsize, mi_row, mi_col);
147 0 : const int pred_flag = pred_segment_id == segment_id;
148 0 : const int pred_context = av1_get_pred_context_seg_id(xd);
149 :
150 : // Store the prediction status for this mb and update counts
151 : // as appropriate
152 0 : xd->mi[0]->mbmi.seg_id_predicted = pred_flag;
153 0 : temporal_predictor_count[pred_context][pred_flag]++;
154 :
155 : // Update the "unpredicted" segment count
156 0 : if (!pred_flag) t_unpred_seg_counts[segment_id]++;
157 : }
158 : }
159 :
160 0 : static void count_segs_sb(const AV1_COMMON *cm, MACROBLOCKD *xd,
161 : const TileInfo *tile, MODE_INFO **mi,
162 : unsigned *no_pred_segcounts,
163 : unsigned (*temporal_predictor_count)[2],
164 : unsigned *t_unpred_seg_counts, int mi_row, int mi_col,
165 : BLOCK_SIZE bsize) {
166 0 : const int mis = cm->mi_stride;
167 0 : const int bs = mi_size_wide[bsize], hbs = bs / 2;
168 : #if CONFIG_EXT_PARTITION_TYPES
169 : PARTITION_TYPE partition;
170 : #else
171 : int bw, bh;
172 : #endif // CONFIG_EXT_PARTITION_TYPES
173 :
174 0 : if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;
175 :
176 : #if CONFIG_EXT_PARTITION_TYPES
177 : if (bsize == BLOCK_8X8)
178 : partition = PARTITION_NONE;
179 : else
180 : partition = get_partition(cm, mi_row, mi_col, bsize);
181 : switch (partition) {
182 : case PARTITION_NONE:
183 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
184 : t_unpred_seg_counts, bs, bs, mi_row, mi_col);
185 : break;
186 : case PARTITION_HORZ:
187 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
188 : t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
189 : count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
190 : temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
191 : mi_row + hbs, mi_col);
192 : break;
193 : case PARTITION_VERT:
194 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
195 : t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
196 : count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
197 : temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
198 : mi_col + hbs);
199 : break;
200 : case PARTITION_HORZ_A:
201 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
202 : t_unpred_seg_counts, hbs, hbs, mi_row, mi_col);
203 : count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
204 : temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
205 : mi_row, mi_col + hbs);
206 : count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
207 : temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
208 : mi_row + hbs, mi_col);
209 : break;
210 : case PARTITION_HORZ_B:
211 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
212 : t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
213 : count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
214 : temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
215 : mi_row + hbs, mi_col);
216 : count_segs(cm, xd, tile, mi + hbs + hbs * mis, no_pred_segcounts,
217 : temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
218 : mi_row + hbs, mi_col + hbs);
219 : break;
220 : case PARTITION_VERT_A:
221 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
222 : t_unpred_seg_counts, hbs, hbs, mi_row, mi_col);
223 : count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
224 : temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
225 : mi_row + hbs, mi_col);
226 : count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
227 : temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
228 : mi_col + hbs);
229 : break;
230 : case PARTITION_VERT_B:
231 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
232 : t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
233 : count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
234 : temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
235 : mi_row, mi_col + hbs);
236 : count_segs(cm, xd, tile, mi + hbs + hbs * mis, no_pred_segcounts,
237 : temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
238 : mi_row + hbs, mi_col + hbs);
239 : break;
240 : case PARTITION_SPLIT: {
241 : const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
242 : int n;
243 :
244 : assert(num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type] < bs &&
245 : num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type] < bs);
246 :
247 : for (n = 0; n < 4; n++) {
248 : const int mi_dc = hbs * (n & 1);
249 : const int mi_dr = hbs * (n >> 1);
250 :
251 : count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts,
252 : temporal_predictor_count, t_unpred_seg_counts,
253 : mi_row + mi_dr, mi_col + mi_dc, subsize);
254 : }
255 : } break;
256 : default: assert(0);
257 : }
258 : #else
259 0 : bw = mi_size_wide[mi[0]->mbmi.sb_type];
260 0 : bh = mi_size_high[mi[0]->mbmi.sb_type];
261 :
262 0 : if (bw == bs && bh == bs) {
263 0 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
264 : t_unpred_seg_counts, bs, bs, mi_row, mi_col);
265 0 : } else if (bw == bs && bh < bs) {
266 0 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
267 : t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
268 0 : count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
269 : temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
270 : mi_row + hbs, mi_col);
271 0 : } else if (bw < bs && bh == bs) {
272 0 : count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
273 : t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
274 0 : count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
275 : temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
276 : mi_col + hbs);
277 : } else {
278 0 : const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
279 : int n;
280 :
281 0 : assert(bw < bs && bh < bs);
282 :
283 0 : for (n = 0; n < 4; n++) {
284 0 : const int mi_dc = hbs * (n & 1);
285 0 : const int mi_dr = hbs * (n >> 1);
286 :
287 0 : count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts,
288 : temporal_predictor_count, t_unpred_seg_counts,
289 : mi_row + mi_dr, mi_col + mi_dc, subsize);
290 : }
291 : }
292 : #endif // CONFIG_EXT_PARTITION_TYPES
293 : }
294 :
295 0 : void av1_choose_segmap_coding_method(AV1_COMMON *cm, MACROBLOCKD *xd) {
296 0 : struct segmentation *seg = &cm->seg;
297 0 : struct segmentation_probs *segp = &cm->fc->seg;
298 :
299 : int no_pred_cost;
300 0 : int t_pred_cost = INT_MAX;
301 :
302 : int i, tile_col, tile_row, mi_row, mi_col;
303 : #if CONFIG_TILE_GROUPS
304 0 : const int probwt = cm->num_tg;
305 : #else
306 : const int probwt = 1;
307 : #endif
308 :
309 0 : unsigned(*temporal_predictor_count)[2] = cm->counts.seg.pred;
310 0 : unsigned *no_pred_segcounts = cm->counts.seg.tree_total;
311 0 : unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred;
312 :
313 : aom_prob no_pred_tree[SEG_TREE_PROBS];
314 : aom_prob t_pred_tree[SEG_TREE_PROBS];
315 : aom_prob t_nopred_prob[PREDICTION_PROBS];
316 :
317 : (void)xd;
318 :
319 : // We are about to recompute all the segment counts, so zero the accumulators.
320 0 : av1_zero(cm->counts.seg);
321 :
322 : // First of all generate stats regarding how well the last segment map
323 : // predicts this one
324 0 : for (tile_row = 0; tile_row < cm->tile_rows; tile_row++) {
325 : TileInfo tile_info;
326 0 : av1_tile_set_row(&tile_info, cm, tile_row);
327 0 : for (tile_col = 0; tile_col < cm->tile_cols; tile_col++) {
328 : MODE_INFO **mi_ptr;
329 0 : av1_tile_set_col(&tile_info, cm, tile_col);
330 : #if CONFIG_TILE_GROUPS && CONFIG_DEPENDENT_HORZTILES
331 : av1_tile_set_tg_boundary(&tile_info, cm, tile_row, tile_col);
332 : #endif
333 0 : mi_ptr = cm->mi_grid_visible + tile_info.mi_row_start * cm->mi_stride +
334 0 : tile_info.mi_col_start;
335 0 : for (mi_row = tile_info.mi_row_start; mi_row < tile_info.mi_row_end;
336 0 : mi_row += cm->mib_size, mi_ptr += cm->mib_size * cm->mi_stride) {
337 0 : MODE_INFO **mi = mi_ptr;
338 0 : for (mi_col = tile_info.mi_col_start; mi_col < tile_info.mi_col_end;
339 0 : mi_col += cm->mib_size, mi += cm->mib_size) {
340 0 : count_segs_sb(cm, xd, &tile_info, mi, no_pred_segcounts,
341 : temporal_predictor_count, t_unpred_seg_counts, mi_row,
342 0 : mi_col, cm->sb_size);
343 : }
344 : }
345 : }
346 : }
347 :
348 : // Work out probability tree for coding segments without prediction
349 : // and the cost.
350 0 : calc_segtree_probs(no_pred_segcounts, no_pred_tree, segp->tree_probs, probwt);
351 0 : no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
352 :
353 : // Key frames cannot use temporal prediction
354 0 : if (!frame_is_intra_only(cm) && !cm->error_resilient_mode) {
355 : // Work out probability tree for coding those segments not
356 : // predicted using the temporal method and the cost.
357 0 : calc_segtree_probs(t_unpred_seg_counts, t_pred_tree, segp->tree_probs,
358 : probwt);
359 0 : t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
360 :
361 : // Add in the cost of the signaling for each prediction context.
362 0 : for (i = 0; i < PREDICTION_PROBS; i++) {
363 0 : const int count0 = temporal_predictor_count[i][0];
364 0 : const int count1 = temporal_predictor_count[i][1];
365 :
366 0 : t_nopred_prob[i] = get_binary_prob(count0, count1);
367 0 : av1_prob_diff_update_savings_search(
368 0 : temporal_predictor_count[i], segp->pred_probs[i], &t_nopred_prob[i],
369 : DIFF_UPDATE_PROB, probwt);
370 :
371 : // Add in the predictor signaling cost
372 0 : t_pred_cost += count0 * av1_cost_zero(t_nopred_prob[i]) +
373 0 : count1 * av1_cost_one(t_nopred_prob[i]);
374 : }
375 : }
376 :
377 : // Now choose which coding method to use.
378 0 : if (t_pred_cost < no_pred_cost) {
379 0 : assert(!cm->error_resilient_mode);
380 0 : seg->temporal_update = 1;
381 : } else {
382 0 : seg->temporal_update = 0;
383 : }
384 0 : }
385 :
386 0 : void av1_reset_segment_features(AV1_COMMON *cm) {
387 0 : struct segmentation *seg = &cm->seg;
388 :
389 : // Set up default state for MB feature flags
390 0 : seg->enabled = 0;
391 0 : seg->update_map = 0;
392 0 : seg->update_data = 0;
393 0 : av1_clearall_segfeatures(seg);
394 0 : }
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