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Port folder renaming changes from AOM

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Manually cherry-picked commits:
ceef058 libvpx->libaom part2
3d26d91 libvpx -> libaom
cfea7dd vp10/ -> av1/
3a8eff7 Fix a build issue for a test
bf4202e Rename vpx to aom

Change-Id: I1b0eb5a40796e3aaf41c58984b4229a439a597dc
This commit is contained in:
Yaowu Xu
2016-08-22 16:08:15 -07:00
parent b1fb998c46
commit c27fc14b02
656 changed files with 3586 additions and 2434 deletions
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378
av1/encoder/segmentation.c Normal file
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/*
* Copyright (c) 2012 The WebM project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include <limits.h>
#include "aom_mem/vpx_mem.h"
#include "av1/common/pred_common.h"
#include "av1/common/tile_common.h"
#include "av1/encoder/cost.h"
#include "av1/encoder/segmentation.h"
#include "av1/encoder/subexp.h"
void vp10_enable_segmentation(struct segmentation *seg) {
seg->enabled = 1;
seg->update_map = 1;
seg->update_data = 1;
}
void vp10_disable_segmentation(struct segmentation *seg) {
seg->enabled = 0;
seg->update_map = 0;
seg->update_data = 0;
}
void vp10_set_segment_data(struct segmentation *seg, signed char *feature_data,
unsigned char abs_delta) {
seg->abs_delta = abs_delta;
memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));
}
void vp10_disable_segfeature(struct segmentation *seg, int segment_id,
SEG_LVL_FEATURES feature_id) {
seg->feature_mask[segment_id] &= ~(1 << feature_id);
}
void vp10_clear_segdata(struct segmentation *seg, int segment_id,
SEG_LVL_FEATURES feature_id) {
seg->feature_data[segment_id][feature_id] = 0;
}
// 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] };
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;
vp10_prob_diff_update_savings_search(
ct, cur_tree_probs[i], &segment_tree_probs[i], DIFF_UPDATE_PROB);
}
}
// Based on set of segment counts and probabilities calculate a cost estimate
static int cost_segmap(unsigned *segcounts, vpx_prob *probs) {
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];
const int c0123 = c01 + c23;
const int c4567 = c45 + c67;
// Cost the top node of the tree
int cost = c0123 * vp10_cost_zero(probs[0]) + c4567 * vp10_cost_one(probs[0]);
// Cost subsequent levels
if (c0123 > 0) {
cost += c01 * vp10_cost_zero(probs[1]) + c23 * vp10_cost_one(probs[1]);
if (c01 > 0)
cost += segcounts[0] * vp10_cost_zero(probs[3]) +
segcounts[1] * vp10_cost_one(probs[3]);
if (c23 > 0)
cost += segcounts[2] * vp10_cost_zero(probs[4]) +
segcounts[3] * vp10_cost_one(probs[4]);
}
if (c4567 > 0) {
cost += c45 * vp10_cost_zero(probs[2]) + c67 * vp10_cost_one(probs[2]);
if (c45 > 0)
cost += segcounts[4] * vp10_cost_zero(probs[5]) +
segcounts[5] * vp10_cost_one(probs[5]);
if (c67 > 0)
cost += segcounts[6] * vp10_cost_zero(probs[6]) +
segcounts[7] * vp10_cost_one(probs[6]);
}
return cost;
}
static void count_segs(const VP10_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 bw, int bh,
int mi_row, int mi_col) {
int segment_id;
if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return;
xd->mi = mi;
segment_id = xd->mi[0]->mbmi.segment_id;
set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols);
// Count the number of hits on each segment with no prediction
no_pred_segcounts[segment_id]++;
// Temporal prediction not allowed on key frames
if (cm->frame_type != KEY_FRAME) {
const BLOCK_SIZE bsize = xd->mi[0]->mbmi.sb_type;
// Test to see if the segment id matches the predicted value.
const int pred_segment_id =
get_segment_id(cm, cm->last_frame_seg_map, bsize, mi_row, mi_col);
const int pred_flag = pred_segment_id == segment_id;
const int pred_context = vp10_get_pred_context_seg_id(xd);
// Store the prediction status for this mb and update counts
// as appropriate
xd->mi[0]->mbmi.seg_id_predicted = pred_flag;
temporal_predictor_count[pred_context][pred_flag]++;
// Update the "unpredicted" segment count
if (!pred_flag) t_unpred_seg_counts[segment_id]++;
}
}
static void count_segs_sb(const VP10_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 = num_8x8_blocks_wide_lookup[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 = 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);
}
}
#endif // CONFIG_EXT_PARTITION_TYPES
}
void vp10_choose_segmap_coding_method(VP10_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;
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;
vpx_prob no_pred_tree[SEG_TREE_PROBS];
vpx_prob t_pred_tree[SEG_TREE_PROBS];
vpx_prob t_nopred_prob[PREDICTION_PROBS];
(void)xd;
// We are about to recompute all the segment counts, so zero the accumulators.
vp10_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;
vp10_tile_set_row(&tile_info, cm, tile_row);
for (tile_col = 0; tile_col < cm->tile_cols; tile_col++) {
MODE_INFO **mi_ptr;
vp10_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);
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];
t_nopred_prob[i] = get_binary_prob(count0, count1);
vp10_prob_diff_update_savings_search(temporal_predictor_count[i],
segp->pred_probs[i],
&t_nopred_prob[i], DIFF_UPDATE_PROB);
// 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;
} else {
seg->temporal_update = 0;
}
}
void vp10_reset_segment_features(VP10_COMMON *cm) {
struct segmentation *seg = &cm->seg;
// Set up default state for MB feature flags
seg->enabled = 0;
seg->update_map = 0;
seg->update_data = 0;
vp10_clearall_segfeatures(seg);
}