64x64 blocksize support.
3.2% gains on std/hd, 1.0% gains on hd. Change-Id: I481d5df23d8a4fc650a5bcba956554490b2bd200
This commit is contained in:
@@ -141,21 +141,57 @@ static int cost_segmap(MACROBLOCKD *xd,
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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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static void count_segs(VP9_COMP *cpi,
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MODE_INFO *mi,
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int *no_pred_segcounts,
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int (*temporal_predictor_count)[2],
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int *t_unpred_seg_counts,
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int mb_size, int mb_row, int mb_col) {
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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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const int segmap_index = mb_row * cm->mb_cols + mb_col;
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const int segment_id = mi->mbmi.segment_id;
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xd->mode_info_context = mi;
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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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xd->mb_to_bottom_edge = ((cm->mb_rows - mb_size - mb_row) * 16) << 3;
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xd->mb_to_right_edge = ((cm->mb_cols - mb_size - mb_col) * 16) << 3;
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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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const 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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const int pred_context = 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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}
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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 i;
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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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@@ -165,9 +201,8 @@ void vp9_choose_segmap_coding_method(VP9_COMP *cpi) {
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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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MODE_INFO *mi_ptr = cm->mi, *mi;
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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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@@ -183,87 +218,57 @@ void vp9_choose_segmap_coding_method(VP9_COMP *cpi) {
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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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for (mb_row = 0; mb_row < cm->mb_rows; mb_row += 4, mi_ptr += 4 * mis) {
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mi = mi_ptr;
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for (mb_col = 0; mb_col < cm->mb_cols; mb_col += 4, mi += 4) {
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#if CONFIG_SUPERBLOCKS && CONFIG_SUPERBLOCKS64
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if (mi->mbmi.sb_type == BLOCK_SIZE_SB64X64) {
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count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
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t_unpred_seg_counts, 4, mb_row, mb_col);
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} else
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#endif
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{
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for (i = 0; i < 4; i++) {
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int x_idx = (i & 1) << 1, y_idx = i & 2;
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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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MODE_INFO *sb_mi = mi + y_idx * mis + x_idx;
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#endif
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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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continue;
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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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if (sb_mi->mbmi.sb_type) {
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assert(sb_mi->mbmi.sb_type == BLOCK_SIZE_SB32X32);
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count_segs(cpi, sb_mi, no_pred_segcounts, temporal_predictor_count,
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t_unpred_seg_counts, 2, mb_row + y_idx, mb_col + x_idx);
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} else
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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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int j;
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for (j = 0; j < 4; j++) {
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const int x_idx_mb = x_idx + (j & 1), y_idx_mb = y_idx + (j >> 1);
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MODE_INFO *mb_mi = mi + x_idx_mb + y_idx_mb * mis;
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if (mb_col + x_idx_mb >= cm->mb_cols ||
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mb_row + y_idx_mb >= cm->mb_rows) {
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continue;
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}
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#if CONFIG_SUPERBLOCKS
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assert(mb_mi->mbmi.sb_type == BLOCK_SIZE_MB16X16);
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#endif
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count_segs(cpi, mb_mi, no_pred_segcounts,
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temporal_predictor_count, t_unpred_seg_counts,
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1, mb_row + y_idx_mb, mb_col + x_idx_mb);
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}
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}
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}
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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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