static void adapt_coef_probs(VP9_COMMON *cm, TX_SIZE tx_size,
unsigned int count_sat,
unsigned int update_factor) {
FRAME_CONTEXT *pre_fc = &cm->frame_contexts[cm->frame_context_idx];
vp9_coeff_probs_model *dst_coef_probs = cm->fc.coef_probs[tx_size];
vp9_coeff_probs_model *pre_coef_probs = pre_fc->coef_probs[tx_size];
vp9_coeff_count_model *coef_counts = cm->counts.coef[tx_size];
unsigned int (*eob_branch_count)[REF_TYPES][COEF_BANDS][PREV_COEF_CONTEXTS] =
cm->counts.eob_branch[tx_size];
int t, i, j, k, l;
unsigned int branch_ct[UNCONSTRAINED_NODES][2];
vp9_prob coef_probs[UNCONSTRAINED_NODES];
for (i = 0; i < BLOCK_TYPES; ++i)
for (j = 0; j < REF_TYPES; ++j)
for (k = 0; k < COEF_BANDS; ++k)
for (l = 0; l < PREV_COEF_CONTEXTS; ++l) {
if (l >= 3 && k == 0)
continue;
vp9_tree_probs_from_distribution(vp9_coefmodel_tree, coef_probs,
branch_ct, coef_counts[i][j][k][l],
0);
branch_ct[0][1] = eob_branch_count[i][j][k][l] - branch_ct[0][0];
coef_probs[0] = get_binary_prob(branch_ct[0][0], branch_ct[0][1]);
for (t = 0; t < UNCONSTRAINED_NODES; ++t)
dst_coef_probs[i][j][k][l][t] = merge_probs(
pre_coef_probs[i][j][k][l][t], coef_probs[t],
branch_ct[t], count_sat, update_factor);
}
}
static void build_tree_distribution(VP9_COMP *cpi, TX_SIZE tx_size,
vp9_coeff_stats *coef_branch_ct,
vp9_coeff_probs_model *coef_probs) {
vp9_coeff_count *coef_counts = cpi->td.rd_counts.coef_counts[tx_size];
unsigned int (*eob_branch_ct)[REF_TYPES][COEF_BANDS][COEFF_CONTEXTS] =
cpi->common.counts.eob_branch[tx_size];
int i, j, k, l, m;
for (i = 0; i < PLANE_TYPES; ++i) {
for (j = 0; j < REF_TYPES; ++j) {
for (k = 0; k < COEF_BANDS; ++k) {
for (l = 0; l < BAND_COEFF_CONTEXTS(k); ++l) {
vp9_tree_probs_from_distribution(vp9_coef_tree,
coef_branch_ct[i][j][k][l],
coef_counts[i][j][k][l]);
coef_branch_ct[i][j][k][l][0][1] = eob_branch_ct[i][j][k][l] -
coef_branch_ct[i][j][k][l][0][0];
for (m = 0; m < UNCONSTRAINED_NODES; ++m)
coef_probs[i][j][k][l][m] = get_binary_prob(
coef_branch_ct[i][j][k][l][m][0],
coef_branch_ct[i][j][k][l][m][1]);
}
}
}
}
}
// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs(unsigned *segcounts,
aom_prob *segment_tree_probs,
const aom_prob *cur_tree_probs,
const int probwt) {
// Work out probabilities of each segment
const unsigned cc[4] = { segcounts[0] + segcounts[1],
segcounts[2] + segcounts[3],
segcounts[4] + segcounts[5],
segcounts[6] + segcounts[7] };
const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] };
int i;
segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]);
segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]);
segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]);
segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
for (i = 0; i < 7; i++) {
const unsigned *ct =
i == 0 ? ccc : i < 3 ? cc + (i & 2) : segcounts + (i - 3) * 2;
av1_prob_diff_update_savings_search(ct, cur_tree_probs[i],
&segment_tree_probs[i],
DIFF_UPDATE_PROB, probwt);
}
}
// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs(unsigned *segcounts,
vpx_prob *segment_tree_probs,
const vpx_prob *cur_tree_probs) {
// Work out probabilities of each segment
const unsigned cc[4] = { segcounts[0] + segcounts[1],
segcounts[2] + segcounts[3],
segcounts[4] + segcounts[5],
segcounts[6] + segcounts[7] };
const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] };
#if CONFIG_MISC_FIXES
int i;
#endif
segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]);
segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]);
segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]);
segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
#if CONFIG_MISC_FIXES
for (i = 0; i < 7; i++) {
const unsigned *ct =
i == 0 ? ccc : i < 3 ? cc + (i & 2) : segcounts + (i - 3) * 2;
vp10_prob_diff_update_savings_search(
ct, cur_tree_probs[i], &segment_tree_probs[i], DIFF_UPDATE_PROB);
}
#else
(void)cur_tree_probs;
#endif
}
static void print_probs(FILE *f, vp9_coeff_accum *context_counters,
int block_types, const char *header) {
int type, ref, band, pt, t;
fprintf(f, "static const vp9_coeff_probs %s = {", header);
type = 0;
#define Newline(x, spaces) (x ? " " : "\n" spaces)
do {
fprintf(f, "%s%s{ /* block Type %d */",
Comma(type), Newline(type, " "), type);
ref = 0;
do {
fprintf(f, "%s%s{ /* %s */",
Comma(band), Newline(band, " "), ref ? "Inter" : "Intra");
band = 0;
do {
fprintf(f, "%s%s{ /* Coeff Band %d */",
Comma(band), Newline(band, " "), band);
pt = 0;
do {
unsigned int branch_ct[ENTROPY_NODES][2];
unsigned int coef_counts[MAX_ENTROPY_TOKENS + 1];
vp9_prob coef_probs[ENTROPY_NODES];
if (pt >= 3 && band == 0)
break;
for (t = 0; t < MAX_ENTROPY_TOKENS + 1; ++t)
coef_counts[t] = context_counters[type][ref][band][pt][t];
vp9_tree_probs_from_distribution(vp9_coef_tree, coef_probs,
branch_ct, coef_counts, 0);
branch_ct[0][1] = coef_counts[MAX_ENTROPY_TOKENS] - branch_ct[0][0];
coef_probs[0] = get_binary_prob(branch_ct[0][0], branch_ct[0][1]);
fprintf(f, "%s\n {", Comma(pt));
t = 0;
do {
fprintf(f, "%s %3d", Comma(t), coef_probs[t]);
} while (++t < ENTROPY_NODES);
fprintf(f, " }");
} while (++pt < PREV_COEF_CONTEXTS);
fprintf(f, "\n }");
} while (++band < COEF_BANDS);
fprintf(f, "\n }");
} while (++ref < REF_TYPES);
fprintf(f, "\n }");
} while (++type < block_types);
fprintf(f, "\n};\n");
}
void vp9_tree_probs_from_distribution(
int n, /* n = size of alphabet */
vp9_token tok [ /* n */ ],
vp9_tree tree,
vp9_prob probs [ /* n-1 */ ],
unsigned int branch_ct [ /* n-1 */ ] [2],
const unsigned int num_events[ /* n */ ]
) {
const int tree_len = n - 1;
int t = 0;
branch_counts(n, tok, tree, branch_ct, num_events);
do {
probs[t] = get_binary_prob(branch_ct[t][0], branch_ct[t][1]);
} while (++t < tree_len);
}
// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs(int *segcounts, vp9_prob *segment_tree_probs) {
// Work out probabilities of each segment
const int c01 = segcounts[0] + segcounts[1];
const int c23 = segcounts[2] + segcounts[3];
const int c45 = segcounts[4] + segcounts[5];
const int c67 = segcounts[6] + segcounts[7];
segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67);
segment_tree_probs[1] = get_binary_prob(c01, c23);
segment_tree_probs[2] = get_binary_prob(c45, c67);
segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
}
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;
}
}
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
}
}
