Fast HEVC Intra Mode Decision Based on RDO Cost Prediction
Bibliographic record
Abstract
High efficiency video coding (HEVC) increases the number of intra coding modes to 35 to provide higher coding efficiency than previous video coding standards. This results in an increased encoder complexity, since there are more modes to be processed by the high resource-demanding rate-distortion optimization (RDO). In this paper, we propose a novel method to reduce the HEVC intra mode decision computational complexity and encoding time. This method is based on the prediction of the RDO cost of intra modes from a low-complexity sum of absolute transformed differences-based cost. By predicting the RDO cost, we are able to exclude non-promising modes from further processing and thereby save substantial computations. Also, a gradient-based method, using the Prewitt operator, is proposed to eliminate the non-relevant directional modes from the list of candidates. For even more complexity reduction, a mode classification is proposed to adaptively reduce chroma intra modes based on block texture. Experimental results show that we achieve a 47.3% encoding time reduction on average with a negligible quality loss of 0.062 dB for the Bjøntegaard delta peak signal-to-noise ratio when we compare our method to the HEVC test model 15.0.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".