Optimizing Non Constant Luminance into Constant Luminance for High Dynamic Range Video Distribution
Bibliographic record
Abstract
To improve compression efficiency, pixels are traditionally represented using a luma and two chroma values. Such a representation aims at separating light from color information. Two methods are usually considered for computing luma values: Non-Constant Luminance (NCL) and Constant Luminance (CL). CL equations have been derived from the luminous efficacy of the used gamut color primaries in the light linear domain. NCL applies the same equations but on perceptually encoded values, thus resulting in lower compression efficiency and hue shifts. However, given the higher hardware complexity for implementing CL, the common operational practice in legacy television distribution is to use NCL. In this paper, our motivation is to derive a new set of equations that provides the compression benefits of CL with the lower complexity of NCL for High Dynamic Range video distribution. Results show that the proposed method increases compression efficiency significantly over the NCL approach while maintaining NCL's cost complexity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".