static void count_segs_sb(VP9_COMP *cpi, MODE_INFO *mi,
int *no_pred_segcounts,
int (*temporal_predictor_count)[2],
int *t_unpred_seg_counts,
int mi_row, int mi_col,
BLOCK_SIZE_TYPE bsize) {
VP9_COMMON *const cm = &cpi->common;
const int mis = cm->mode_info_stride;
int bwl, bhl;
const int bsl = mi_width_log2(bsize), bs = 1 << (bsl - 1);
if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
return;
bwl = mi_width_log2(mi->mbmi.sb_type);
bhl = mi_height_log2(mi->mbmi.sb_type);
if (bwl == bsl && bhl == bsl) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 1 << bsl, 1 << bsl, mi_row, mi_col);
} else if (bwl == bsl && bhl < bsl) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 1 << bsl, bs, mi_row, mi_col);
count_segs(cpi, mi + bs * mis, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 1 << bsl, bs, mi_row + bs, mi_col);
} else if (bwl < bsl && bhl == bsl) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, 1 << bsl, mi_row, mi_col);
count_segs(cpi, mi + bs, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, 1 << bsl, mi_row, mi_col + bs);
} else {
BLOCK_SIZE_TYPE subsize;
int n;
assert(bwl < bsl && bhl < bsl);
if (bsize == BLOCK_SIZE_SB64X64) {
subsize = BLOCK_SIZE_SB32X32;
} else if (bsize == BLOCK_SIZE_SB32X32) {
subsize = BLOCK_SIZE_MB16X16;
} else {
assert(bsize == BLOCK_SIZE_MB16X16);
subsize = BLOCK_SIZE_SB8X8;
}
for (n = 0; n < 4; n++) {
const int y_idx = n >> 1, x_idx = n & 0x01;
count_segs_sb(cpi, mi + y_idx * bs * mis + x_idx * bs,
no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts,
mi_row + y_idx * bs, mi_col + x_idx * bs, subsize);
}
}
}
static void count_segs_sb(const VP9_COMMON *cm, MACROBLOCKD *xd,
const TileInfo *tile, MODE_INFO **mi,
int *no_pred_segcounts,
int (*temporal_predictor_count)[2],
int *t_unpred_seg_counts,
int mi_row, int mi_col,
BLOCK_SIZE bsize) {
const int mis = cm->mi_stride;
int bw, bh;
const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2;
if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
return;
bw = num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type];
bh = num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type];
if (bw == bs && bh == bs) {
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, bs, mi_row, mi_col);
} else if (bw == bs && bh < bs) {
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
mi_row + hbs, mi_col);
} else if (bw < bs && bh == bs) {
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs,
no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts,
hbs, bs, mi_row, mi_col + hbs);
} else {
const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
int n;
assert(bw < bs && bh < bs);
for (n = 0; n < 4; n++) {
const int mi_dc = hbs * (n & 1);
const int mi_dr = hbs * (n >> 1);
count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc],
no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts,
mi_row + mi_dr, mi_col + mi_dc, subsize);
}
}
}
void av1_choose_segmap_coding_method(AV1_COMMON *cm, MACROBLOCKD *xd) {
struct segmentation *seg = &cm->seg;
struct segmentation_probs *segp = &cm->fc->seg;
int no_pred_cost;
int t_pred_cost = INT_MAX;
int i, tile_col, tile_row, mi_row, mi_col;
#if CONFIG_TILE_GROUPS
const int probwt = cm->num_tg;
#else
const int probwt = 1;
#endif
unsigned(*temporal_predictor_count)[2] = cm->counts.seg.pred;
unsigned *no_pred_segcounts = cm->counts.seg.tree_total;
unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred;
aom_prob no_pred_tree[SEG_TREE_PROBS];
aom_prob t_pred_tree[SEG_TREE_PROBS];
aom_prob t_nopred_prob[PREDICTION_PROBS];
(void)xd;
// We are about to recompute all the segment counts, so zero the accumulators.
av1_zero(cm->counts.seg);
// First of all generate stats regarding how well the last segment map
// predicts this one
for (tile_row = 0; tile_row < cm->tile_rows; tile_row++) {
TileInfo tile_info;
av1_tile_set_row(&tile_info, cm, tile_row);
for (tile_col = 0; tile_col < cm->tile_cols; tile_col++) {
MODE_INFO **mi_ptr;
av1_tile_set_col(&tile_info, cm, tile_col);
mi_ptr = cm->mi_grid_visible + tile_info.mi_row_start * cm->mi_stride +
tile_info.mi_col_start;
for (mi_row = tile_info.mi_row_start; mi_row < tile_info.mi_row_end;
mi_row += cm->mib_size, mi_ptr += cm->mib_size * cm->mi_stride) {
MODE_INFO **mi = mi_ptr;
for (mi_col = tile_info.mi_col_start; mi_col < tile_info.mi_col_end;
mi_col += cm->mib_size, mi += cm->mib_size) {
count_segs_sb(cm, xd, &tile_info, mi, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, mi_row,
mi_col, cm->sb_size);
}
}
}
}
// Work out probability tree for coding segments without prediction
// and the cost.
calc_segtree_probs(no_pred_segcounts, no_pred_tree, segp->tree_probs, probwt);
no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
// Key frames cannot use temporal prediction
if (!frame_is_intra_only(cm) && !cm->error_resilient_mode) {
// Work out probability tree for coding those segments not
// predicted using the temporal method and the cost.
calc_segtree_probs(t_unpred_seg_counts, t_pred_tree, segp->tree_probs,
probwt);
t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
// Add in the cost of the signaling for each prediction context.
for (i = 0; i < PREDICTION_PROBS; i++) {
const int count0 = temporal_predictor_count[i][0];
const int count1 = temporal_predictor_count[i][1];
t_nopred_prob[i] = get_binary_prob(count0, count1);
av1_prob_diff_update_savings_search(
temporal_predictor_count[i], segp->pred_probs[i], &t_nopred_prob[i],
DIFF_UPDATE_PROB, probwt);
// Add in the predictor signaling cost
t_pred_cost += count0 * av1_cost_zero(t_nopred_prob[i]) +
count1 * av1_cost_one(t_nopred_prob[i]);
}
}
// Now choose which coding method to use.
if (t_pred_cost < no_pred_cost) {
assert(!cm->error_resilient_mode);
seg->temporal_update = 1;
} else {
seg->temporal_update = 0;
}
}
static void count_segs_sb(const AV1_COMMON *cm, MACROBLOCKD *xd,
const TileInfo *tile, MODE_INFO **mi,
unsigned *no_pred_segcounts,
unsigned (*temporal_predictor_count)[2],
unsigned *t_unpred_seg_counts, int mi_row, int mi_col,
BLOCK_SIZE bsize) {
const int mis = cm->mi_stride;
const int bs = mi_size_wide[bsize], hbs = bs / 2;
#if CONFIG_EXT_PARTITION_TYPES
PARTITION_TYPE partition;
#else
int bw, bh;
#endif // CONFIG_EXT_PARTITION_TYPES
if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;
#if CONFIG_EXT_PARTITION_TYPES
if (bsize == BLOCK_8X8)
partition = PARTITION_NONE;
else
partition = get_partition(cm, mi_row, mi_col, bsize);
switch (partition) {
case PARTITION_NONE:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, bs, mi_row, mi_col);
break;
case PARTITION_HORZ:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
mi_row + hbs, mi_col);
break;
case PARTITION_VERT:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
mi_col + hbs);
break;
case PARTITION_HORZ_A:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, hbs, hbs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
mi_row, mi_col + hbs);
count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
mi_row + hbs, mi_col);
break;
case PARTITION_HORZ_B:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
mi_row + hbs, mi_col);
count_segs(cm, xd, tile, mi + hbs + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
mi_row + hbs, mi_col + hbs);
break;
case PARTITION_VERT_A:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, hbs, hbs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
mi_row + hbs, mi_col);
count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
mi_col + hbs);
break;
case PARTITION_VERT_B:
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
mi_row, mi_col + hbs);
count_segs(cm, xd, tile, mi + hbs + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, hbs,
mi_row + hbs, mi_col + hbs);
break;
case PARTITION_SPLIT: {
const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
int n;
assert(num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type] < bs &&
num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type] < bs);
for (n = 0; n < 4; n++) {
const int mi_dc = hbs * (n & 1);
const int mi_dr = hbs * (n >> 1);
count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts,
mi_row + mi_dr, mi_col + mi_dc, subsize);
}
} break;
default: assert(0);
}
#else
bw = mi_size_wide[mi[0]->mbmi.sb_type];
bh = mi_size_high[mi[0]->mbmi.sb_type];
if (bw == bs && bh == bs) {
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, bs, mi_row, mi_col);
} else if (bw == bs && bh < bs) {
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
mi_row + hbs, mi_col);
} else if (bw < bs && bh == bs) {
count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row,
mi_col + hbs);
} else {
const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
int n;
assert(bw < bs && bh < bs);
for (n = 0; n < 4; n++) {
const int mi_dc = hbs * (n & 1);
const int mi_dr = hbs * (n >> 1);
count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts,
mi_row + mi_dr, mi_col + mi_dc, subsize);
}
}
#endif // CONFIG_EXT_PARTITION_TYPES
}
void vp9_choose_segmap_coding_method(VP9_COMP *cpi) {
VP9_COMMON *const cm = &cpi->common;
MACROBLOCKD *const xd = &cpi->mb.e_mbd;
int no_pred_cost;
int t_pred_cost = INT_MAX;
int i;
int tile_col, mi_row, mi_col;
int temporal_predictor_count[PREDICTION_PROBS][2];
int no_pred_segcounts[MAX_MB_SEGMENTS];
int t_unpred_seg_counts[MAX_MB_SEGMENTS];
vp9_prob no_pred_tree[MB_SEG_TREE_PROBS];
vp9_prob t_pred_tree[MB_SEG_TREE_PROBS];
vp9_prob t_nopred_prob[PREDICTION_PROBS];
const int mis = cm->mode_info_stride;
MODE_INFO *mi_ptr, *mi;
// Set default state for the segment tree probabilities and the
// temporal coding probabilities
vpx_memset(xd->mb_segment_tree_probs, 255, sizeof(xd->mb_segment_tree_probs));
vpx_memset(cm->segment_pred_probs, 255, sizeof(cm->segment_pred_probs));
vpx_memset(no_pred_segcounts, 0, sizeof(no_pred_segcounts));
vpx_memset(t_unpred_seg_counts, 0, sizeof(t_unpred_seg_counts));
vpx_memset(temporal_predictor_count, 0, sizeof(temporal_predictor_count));
// First of all generate stats regarding how well the last segment map
// predicts this one
for (tile_col = 0; tile_col < cm->tile_columns; tile_col++) {
vp9_get_tile_col_offsets(cm, tile_col);
mi_ptr = cm->mi + cm->cur_tile_mi_col_start;
for (mi_row = 0; mi_row < cm->mi_rows;
mi_row += 8, mi_ptr += 8 * mis) {
mi = mi_ptr;
for (mi_col = cm->cur_tile_mi_col_start;
mi_col < cm->cur_tile_mi_col_end;
mi_col += 8, mi += 8) {
count_segs_sb(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, mi_row, mi_col, BLOCK_SIZE_SB64X64);
}
}
}
// Work out probability tree for coding segments without prediction
// and the cost.
calc_segtree_probs(xd, no_pred_segcounts, no_pred_tree);
no_pred_cost = cost_segmap(xd, no_pred_segcounts, no_pred_tree);
// Key frames cannot use temporal prediction
if (cm->frame_type != KEY_FRAME) {
// Work out probability tree for coding those segments not
// predicted using the temporal method and the cost.
calc_segtree_probs(xd, t_unpred_seg_counts, t_pred_tree);
t_pred_cost = cost_segmap(xd, t_unpred_seg_counts, t_pred_tree);
// Add in the cost of the signalling for each prediction context
for (i = 0; i < PREDICTION_PROBS; i++) {
const int count0 = temporal_predictor_count[i][0];
const int count1 = temporal_predictor_count[i][1];
t_nopred_prob[i] = get_binary_prob(count0, count1);
// Add in the predictor signaling cost
t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +
count1 * vp9_cost_one(t_nopred_prob[i]);
}
}
// Now choose which coding method to use.
if (t_pred_cost < no_pred_cost) {
cm->temporal_update = 1;
vpx_memcpy(xd->mb_segment_tree_probs, t_pred_tree, sizeof(t_pred_tree));
vpx_memcpy(cm->segment_pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
} else {
cm->temporal_update = 0;
vpx_memcpy(xd->mb_segment_tree_probs, no_pred_tree, sizeof(no_pred_tree));
}
}
void vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) {
struct segmentation *seg = &cm->seg;
int no_pred_cost;
int t_pred_cost = INT_MAX;
int i, tile_col, mi_row, mi_col;
int temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };
int no_pred_segcounts[MAX_SEGMENTS] = { 0 };
int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
vp9_prob no_pred_tree[SEG_TREE_PROBS];
vp9_prob t_pred_tree[SEG_TREE_PROBS];
vp9_prob t_nopred_prob[PREDICTION_PROBS];
// Set default state for the segment tree probabilities and the
// temporal coding probabilities
memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
memset(seg->pred_probs, 255, sizeof(seg->pred_probs));
// First of all generate stats regarding how well the last segment map
// predicts this one
for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {
TileInfo tile;
MODE_INFO **mi_ptr;
vp9_tile_init(&tile, cm, 0, tile_col);
mi_ptr = cm->mi_grid_visible + tile.mi_col_start;
for (mi_row = 0; mi_row < cm->mi_rows;
mi_row += 8, mi_ptr += 8 * cm->mi_stride) {
MODE_INFO **mi = mi_ptr;
for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;
mi_col += 8, mi += 8)
count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts,
mi_row, mi_col, BLOCK_64X64);
}
}
// Work out probability tree for coding segments without prediction
// and the cost.
calc_segtree_probs(no_pred_segcounts, no_pred_tree);
no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
// Key frames cannot use temporal prediction
if (!frame_is_intra_only(cm)) {
// Work out probability tree for coding those segments not
// predicted using the temporal method and the cost.
calc_segtree_probs(t_unpred_seg_counts, t_pred_tree);
t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
// Add in the cost of the signaling for each prediction context.
for (i = 0; i < PREDICTION_PROBS; i++) {
const int count0 = temporal_predictor_count[i][0];
const int count1 = temporal_predictor_count[i][1];
t_nopred_prob[i] = get_binary_prob(count0, count1);
// Add in the predictor signaling cost
t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +
count1 * vp9_cost_one(t_nopred_prob[i]);
}
}
// Now choose which coding method to use.
if (t_pred_cost < no_pred_cost) {
seg->temporal_update = 1;
memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree));
memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
} else {
seg->temporal_update = 0;
memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree));
}
}
void vp10_choose_segmap_coding_method(VP10_COMMON *cm, MACROBLOCKD *xd) {
struct segmentation *seg = &cm->seg;
#if CONFIG_MISC_FIXES
struct segmentation_probs *segp = &cm->fc->seg;
#else
struct segmentation_probs *segp = &cm->segp;
#endif
int no_pred_cost;
int t_pred_cost = INT_MAX;
int i, tile_col, mi_row, mi_col;
#if CONFIG_MISC_FIXES
unsigned(*temporal_predictor_count)[2] = cm->counts.seg.pred;
unsigned *no_pred_segcounts = cm->counts.seg.tree_total;
unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred;
#else
unsigned temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };
unsigned no_pred_segcounts[MAX_SEGMENTS] = { 0 };
unsigned t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
#endif
vpx_prob no_pred_tree[SEG_TREE_PROBS];
vpx_prob t_pred_tree[SEG_TREE_PROBS];
vpx_prob t_nopred_prob[PREDICTION_PROBS];
#if CONFIG_MISC_FIXES
(void)xd;
#else
// Set default state for the segment tree probabilities and the
// temporal coding probabilities
memset(segp->tree_probs, 255, sizeof(segp->tree_probs));
memset(segp->pred_probs, 255, sizeof(segp->pred_probs));
#endif
// First of all generate stats regarding how well the last segment map
// predicts this one
for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {
TileInfo tile;
MODE_INFO **mi_ptr;
vp10_tile_init(&tile, cm, 0, tile_col);
mi_ptr = cm->mi_grid_visible + tile.mi_col_start;
for (mi_row = 0; mi_row < cm->mi_rows;
mi_row += 8, mi_ptr += 8 * cm->mi_stride) {
MODE_INFO **mi = mi_ptr;
for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;
mi_col += 8, mi += 8)
count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, mi_row,
mi_col, BLOCK_64X64);
}
}
// Work out probability tree for coding segments without prediction
// and the cost.
calc_segtree_probs(no_pred_segcounts, no_pred_tree, segp->tree_probs);
no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
// Key frames cannot use temporal prediction
if (!frame_is_intra_only(cm) && !cm->error_resilient_mode) {
// Work out probability tree for coding those segments not
// predicted using the temporal method and the cost.
calc_segtree_probs(t_unpred_seg_counts, t_pred_tree, segp->tree_probs);
t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
// Add in the cost of the signaling for each prediction context.
for (i = 0; i < PREDICTION_PROBS; i++) {
const int count0 = temporal_predictor_count[i][0];
const int count1 = temporal_predictor_count[i][1];
#if CONFIG_MISC_FIXES
vp10_prob_diff_update_savings_search(temporal_predictor_count[i],
segp->pred_probs[i],
&t_nopred_prob[i], DIFF_UPDATE_PROB);
#else
t_nopred_prob[i] = get_binary_prob(count0, count1);
#endif
// Add in the predictor signaling cost
t_pred_cost += count0 * vp10_cost_zero(t_nopred_prob[i]) +
count1 * vp10_cost_one(t_nopred_prob[i]);
}
}
// Now choose which coding method to use.
if (t_pred_cost < no_pred_cost) {
assert(!cm->error_resilient_mode);
seg->temporal_update = 1;
#if !CONFIG_MISC_FIXES
memcpy(segp->tree_probs, t_pred_tree, sizeof(t_pred_tree));
memcpy(segp->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
#endif
} else {
seg->temporal_update = 0;
#if !CONFIG_MISC_FIXES
memcpy(segp->tree_probs, no_pred_tree, sizeof(no_pred_tree));
#endif
}
}
