Bibliographic record
Abstract
Recent advances in video capturing and display technologies, along with the exponentially increasing demand of video services, challenge the video coding research community to design new algorithms able to significantly improve the compression performance of the current H.264/AVC standard. This target is currently gaining evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical properties for optimization tasks. We propose a perceptual video coding method to improve upon the current HEVC based on an SSIM-inspired divisive normalization scheme as an attempt to transform the DCT domain frame prediction residuals to a perceptually uniform space before encoding. Based on the residual divisive normalization process, we define a distortion model for mode selection and show that such a divisive normalization strategy largely simplifies the subsequent perceptual rate-distortion optimization procedure. We further adjust the divisive normalization factors based on local content of the video frame. Experiments show that the proposed scheme can achieve significant gain in terms of rate-SSIM performance when compared with HEVC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".