A novel parallel deblocking filtering strategy for HEVC/H.265 based on GPU
Bibliographic record
Abstract
Summary The deblocking filter inhigh‐efficiency video coding(HEVC) has huge computational complexity because of its high content‐adaptive coding structure as well as high‐definition. Parallelization for it based on massively parallel architectures such asgraphics processing unitbecomes an urgent demand. However, a large number of conditional branches and data dependencies severely hinder its efficient parallelization. In this paper, a novel parallel optimization strategy based on graphics processing unit is presented for concurrent deblocking in HEVC/H.265 standard to improve the parallel performance. First, by reducing various conditional branches, a normalization mechanism for instruction stream based on feature vector is proposed, which improves the efficiency of boundary strength computation dramatically. The idea can also be applied to edge discrimination. Second, a parallel mechanism based on an adaptive post‐correction is presented to process vertical and horizontal edges filtering concurrently, which improves the processing speed obviously, while producing negligible quality loss. Experimental results show that the strategy presented outperforms the existing state‐of‐the‐art method with accelerating factor up to 32. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".