4d0ec7aacd
Add a function clip_pixel() to clip a pixel value to the [0,255] range of allowed values, and use this where-ever appropriate (e.g. prediction, reconstruction). Likewise, consistently use the recently added function clip_prob(), which calculates a binary probability in the [1,255] range. If possible, try to use get_prob() or its sister get_binary_prob() to calculate binary probabilities, for consistency. Since in some places, this means that binary probability calculations are changed (we use {255,256}*count0/(total) in a range of places, and all of these are now changed to use 256*count0+(total>>1)/total), this changes the encoding result, so this patch warrants some extensive testing. Change-Id: Ibeeff8d886496839b8e0c0ace9ccc552351f7628
307 lines
10 KiB
C
307 lines
10 KiB
C
/*
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* Copyright (c) 2012 The WebM project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "limits.h"
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#include "vpx_mem/vpx_mem.h"
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#include "vp9/encoder/vp9_segmentation.h"
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#include "vp9/common/vp9_pred_common.h"
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void vp9_update_gf_useage_maps(VP9_COMP *cpi, VP9_COMMON *cm, MACROBLOCK *x) {
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int mb_row, mb_col;
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MODE_INFO *this_mb_mode_info = cm->mi;
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x->gf_active_ptr = (signed char *)cpi->gf_active_flags;
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if ((cm->frame_type == KEY_FRAME) || (cm->refresh_golden_frame)) {
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// Reset Gf useage monitors
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vpx_memset(cpi->gf_active_flags, 1, (cm->mb_rows * cm->mb_cols));
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cpi->gf_active_count = cm->mb_rows * cm->mb_cols;
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} else {
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// for each macroblock row in image
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for (mb_row = 0; mb_row < cm->mb_rows; mb_row++) {
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// for each macroblock col in image
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for (mb_col = 0; mb_col < cm->mb_cols; mb_col++) {
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// If using golden then set GF active flag if not already set.
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// If using last frame 0,0 mode then leave flag as it is
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// else if using non 0,0 motion or intra modes then clear
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// flag if it is currently set
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if ((this_mb_mode_info->mbmi.ref_frame == GOLDEN_FRAME) ||
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(this_mb_mode_info->mbmi.ref_frame == ALTREF_FRAME)) {
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if (*(x->gf_active_ptr) == 0) {
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*(x->gf_active_ptr) = 1;
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cpi->gf_active_count++;
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}
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} else if ((this_mb_mode_info->mbmi.mode != ZEROMV) &&
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*(x->gf_active_ptr)) {
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*(x->gf_active_ptr) = 0;
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cpi->gf_active_count--;
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}
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x->gf_active_ptr++; // Step onto next entry
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this_mb_mode_info++; // skip to next mb
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}
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// this is to account for the border
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this_mb_mode_info++;
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}
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}
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}
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void vp9_enable_segmentation(VP9_PTR ptr) {
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VP9_COMP *cpi = (VP9_COMP *)(ptr);
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// Set the appropriate feature bit
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cpi->mb.e_mbd.segmentation_enabled = 1;
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cpi->mb.e_mbd.update_mb_segmentation_map = 1;
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cpi->mb.e_mbd.update_mb_segmentation_data = 1;
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}
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void vp9_disable_segmentation(VP9_PTR ptr) {
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VP9_COMP *cpi = (VP9_COMP *)(ptr);
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// Clear the appropriate feature bit
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cpi->mb.e_mbd.segmentation_enabled = 0;
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}
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void vp9_set_segmentation_map(VP9_PTR ptr,
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unsigned char *segmentation_map) {
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VP9_COMP *cpi = (VP9_COMP *)(ptr);
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// Copy in the new segmentation map
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vpx_memcpy(cpi->segmentation_map, segmentation_map,
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(cpi->common.mb_rows * cpi->common.mb_cols));
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// Signal that the map should be updated.
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cpi->mb.e_mbd.update_mb_segmentation_map = 1;
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cpi->mb.e_mbd.update_mb_segmentation_data = 1;
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}
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void vp9_set_segment_data(VP9_PTR ptr,
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signed char *feature_data,
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unsigned char abs_delta) {
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VP9_COMP *cpi = (VP9_COMP *)(ptr);
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cpi->mb.e_mbd.mb_segment_abs_delta = abs_delta;
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vpx_memcpy(cpi->mb.e_mbd.segment_feature_data, feature_data,
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sizeof(cpi->mb.e_mbd.segment_feature_data));
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// TBD ?? Set the feature mask
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// vpx_memcpy(cpi->mb.e_mbd.segment_feature_mask, 0,
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// sizeof(cpi->mb.e_mbd.segment_feature_mask));
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}
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// Based on set of segment counts calculate a probability tree
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static void calc_segtree_probs(MACROBLOCKD *xd,
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int *segcounts,
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vp9_prob *segment_tree_probs) {
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int count1, count2;
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// Total count for all segments
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count1 = segcounts[0] + segcounts[1];
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count2 = segcounts[2] + segcounts[3];
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// Work out probabilities of each segment
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segment_tree_probs[0] = get_binary_prob(count1, count2);
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segment_tree_probs[1] = get_prob(segcounts[0], count1);
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segment_tree_probs[2] = get_prob(segcounts[2], count2);
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}
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// Based on set of segment counts and probabilities calculate a cost estimate
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static int cost_segmap(MACROBLOCKD *xd,
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int *segcounts,
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vp9_prob *probs) {
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int cost;
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int count1, count2;
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// Cost the top node of the tree
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count1 = segcounts[0] + segcounts[1];
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count2 = segcounts[2] + segcounts[3];
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cost = count1 * vp9_cost_zero(probs[0]) +
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count2 * vp9_cost_one(probs[0]);
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// Now add the cost of each individual segment branch
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if (count1 > 0)
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cost += segcounts[0] * vp9_cost_zero(probs[1]) +
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segcounts[1] * vp9_cost_one(probs[1]);
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if (count2 > 0)
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cost += segcounts[2] * vp9_cost_zero(probs[2]) +
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segcounts[3] * vp9_cost_one(probs[2]);
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return cost;
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}
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void vp9_choose_segmap_coding_method(VP9_COMP *cpi) {
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VP9_COMMON *const cm = &cpi->common;
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MACROBLOCKD *const xd = &cpi->mb.e_mbd;
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int i;
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int no_pred_cost;
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int t_pred_cost = INT_MAX;
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int pred_context;
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int mb_row, mb_col;
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int segmap_index = 0;
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unsigned char segment_id;
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int temporal_predictor_count[PREDICTION_PROBS][2];
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int no_pred_segcounts[MAX_MB_SEGMENTS];
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int t_unpred_seg_counts[MAX_MB_SEGMENTS];
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vp9_prob no_pred_tree[MB_FEATURE_TREE_PROBS];
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vp9_prob t_pred_tree[MB_FEATURE_TREE_PROBS];
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vp9_prob t_nopred_prob[PREDICTION_PROBS];
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#if CONFIG_SUPERBLOCKS
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const int mis = cm->mode_info_stride;
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#endif
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// Set default state for the segment tree probabilities and the
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// temporal coding probabilities
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vpx_memset(xd->mb_segment_tree_probs, 255,
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sizeof(xd->mb_segment_tree_probs));
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vpx_memset(cm->segment_pred_probs, 255,
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sizeof(cm->segment_pred_probs));
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vpx_memset(no_pred_segcounts, 0, sizeof(no_pred_segcounts));
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vpx_memset(t_unpred_seg_counts, 0, sizeof(t_unpred_seg_counts));
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vpx_memset(temporal_predictor_count, 0, sizeof(temporal_predictor_count));
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// First of all generate stats regarding how well the last segment map
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// predicts this one
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// Initialize macroblock decoder mode info context for the first mb
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// in the frame
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xd->mode_info_context = cm->mi;
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for (mb_row = 0; mb_row < cm->mb_rows; mb_row += 2) {
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for (mb_col = 0; mb_col < cm->mb_cols; mb_col += 2) {
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for (i = 0; i < 4; i++) {
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static const int dx[4] = { +1, -1, +1, +1 };
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static const int dy[4] = { 0, +1, 0, -1 };
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int x_idx = i & 1, y_idx = i >> 1;
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if (mb_col + x_idx >= cm->mb_cols ||
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mb_row + y_idx >= cm->mb_rows) {
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goto end;
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}
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xd->mb_to_top_edge = -((mb_row * 16) << 3);
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xd->mb_to_left_edge = -((mb_col * 16) << 3);
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segmap_index = (mb_row + y_idx) * cm->mb_cols + mb_col + x_idx;
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segment_id = xd->mode_info_context->mbmi.segment_id;
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#if CONFIG_SUPERBLOCKS
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if (xd->mode_info_context->mbmi.encoded_as_sb) {
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if (mb_col + 1 < cm->mb_cols)
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segment_id = segment_id &&
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xd->mode_info_context[1].mbmi.segment_id;
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if (mb_row + 1 < cm->mb_rows) {
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segment_id = segment_id &&
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xd->mode_info_context[mis].mbmi.segment_id;
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if (mb_col + 1 < cm->mb_cols)
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segment_id = segment_id &&
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xd->mode_info_context[mis + 1].mbmi.segment_id;
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}
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xd->mb_to_bottom_edge = ((cm->mb_rows - 2 - mb_row) * 16) << 3;
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xd->mb_to_right_edge = ((cm->mb_cols - 2 - mb_col) * 16) << 3;
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} else {
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#endif
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xd->mb_to_bottom_edge = ((cm->mb_rows - 1 - mb_row) * 16) << 3;
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xd->mb_to_right_edge = ((cm->mb_cols - 1 - mb_col) * 16) << 3;
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#if CONFIG_SUPERBLOCKS
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}
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#endif
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// Count the number of hits on each segment with no prediction
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no_pred_segcounts[segment_id]++;
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// Temporal prediction not allowed on key frames
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if (cm->frame_type != KEY_FRAME) {
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// Test to see if the segment id matches the predicted value.
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int seg_predicted =
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(segment_id == vp9_get_pred_mb_segid(cm, xd, segmap_index));
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// Get the segment id prediction context
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pred_context =
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vp9_get_pred_context(cm, xd, PRED_SEG_ID);
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// Store the prediction status for this mb and update counts
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// as appropriate
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vp9_set_pred_flag(xd, PRED_SEG_ID, seg_predicted);
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temporal_predictor_count[pred_context][seg_predicted]++;
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if (!seg_predicted)
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// Update the "unpredicted" segment count
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t_unpred_seg_counts[segment_id]++;
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}
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#if CONFIG_SUPERBLOCKS
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if (xd->mode_info_context->mbmi.encoded_as_sb) {
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assert(!i);
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xd->mode_info_context += 2;
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break;
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}
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#endif
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end:
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xd->mode_info_context += dx[i] + dy[i] * cm->mode_info_stride;
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}
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}
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// this is to account for the border in mode_info_context
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xd->mode_info_context -= mb_col;
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xd->mode_info_context += cm->mode_info_stride * 2;
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}
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// Work out probability tree for coding segments without prediction
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// and the cost.
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calc_segtree_probs(xd, no_pred_segcounts, no_pred_tree);
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no_pred_cost = cost_segmap(xd, no_pred_segcounts, no_pred_tree);
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// Key frames cannot use temporal prediction
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if (cm->frame_type != KEY_FRAME) {
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// Work out probability tree for coding those segments not
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// predicted using the temporal method and the cost.
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calc_segtree_probs(xd, t_unpred_seg_counts, t_pred_tree);
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t_pred_cost = cost_segmap(xd, t_unpred_seg_counts, t_pred_tree);
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// Add in the cost of the signalling for each prediction context
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for (i = 0; i < PREDICTION_PROBS; i++) {
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t_nopred_prob[i] = get_binary_prob(temporal_predictor_count[i][0],
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temporal_predictor_count[i][1]);
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// Add in the predictor signaling cost
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t_pred_cost += (temporal_predictor_count[i][0] *
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vp9_cost_zero(t_nopred_prob[i])) +
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(temporal_predictor_count[i][1] *
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vp9_cost_one(t_nopred_prob[i]));
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}
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}
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// Now choose which coding method to use.
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if (t_pred_cost < no_pred_cost) {
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cm->temporal_update = 1;
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vpx_memcpy(xd->mb_segment_tree_probs,
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t_pred_tree, sizeof(t_pred_tree));
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vpx_memcpy(&cm->segment_pred_probs,
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t_nopred_prob, sizeof(t_nopred_prob));
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} else {
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cm->temporal_update = 0;
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vpx_memcpy(xd->mb_segment_tree_probs,
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no_pred_tree, sizeof(no_pred_tree));
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}
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}
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