Color-Sensitivity-Based Rate-Distortion Optimization for H.265/HEVC
Bibliographic record
Abstract
Rate-Distortion Optimization (RDO) is an important step in video coding to achieve the best quality under a certain compression ratio constraint. The traditional RDO assigns equal importance to different color components. However, Human Visual System (HVS) has different sensitivities to different components. In this paper, the color-sensitivity-based combined PSNR (CSPSNR) is utilized as the distortion measurement in the process of RDO, where the characteristics of the color sensitivities of HVS are taken into account. Firstly, the distortion weights of luma and chroma components are derived from the criterion of maximizing CSPSNR. Then Lagrange multiplier and quantization parameter (QP) are adjusted according to the variation of distortion weights among different components. Finally, the CSPSNR-based RDO (CSRDO) adaptively calculates the RD costs of luma and chroma components under different sampling rates to improve the coding efficiency of the whole sequence. Experimental results in H.265/HEVC demonstrate that the proposed method can achieve 3.11% and 3.58% BD-RATE gain for AI and RA configurations in terms of CSPSNR on average.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".