SSIM-Motivated Rate-Distortion Optimization for Video Coding
Bibliographic record
Abstract
We propose a rate-distortion optimization (RDO) scheme based on the structural similarity (SSIM) index, which was found to be a better indicator of perceived image quality than mean-squared error, but has not been fully exploited in the context of image and video coding. At the frame level, an adaptive Lagrange multiplier selection method is proposed based on a novel reduced-reference statistical SSIM estimation algorithm and a rate model that combines the side information with the entropy of the transformed residuals. At the macroblock level, the Lagrange multiplier is further adjusted based on an information theoretical approach that takes into account both the motion information content and perceptual uncertainty of visual speed perception. Finally, the mode for H.264/AVC coding is selected by the SSIM index and the adjusted Lagrange multiplier. Extensive experiments show that the proposed scheme can achieve significantly better rate-SSIM performance and provide better visual quality than conventional RDO coding schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".