SSIM-inspired two-pass rate control for High Efficiency Video Coding
Bibliographic record
Abstract
We propose a perceptual two-pass rate control scheme for High Efficiency Video Coding (HEVC). The target bits are optimally allocated by hierarchically constructing a perceptual uniform space derived based on an SSIM-inspired divisive normalization mechanism for each group of pictures (GoP), each frame, and each coding unit (CU). The Lagrange multiplier λ, which controls the trade-off between perceptual distortion and bit rate, is adopted as the GoP level complexity measure. After the first pass compression, Laplacian based rate and perceptual distortion models are established to adaptively derive λ, and the target bits are dynamically allocated by maintaining an uniform Lagrange multiplier level through λ equalization. Within each GoP, rate control is further performed at frame and CU levels in the perceptually uniform space. Extensive simulations verify that, the proposed scheme can achieve high accuracy rate control and superior rate-SSIM performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".