Evaluation of chroma subsampling for high dynamic range video compression
Bibliographic record
Abstract
Subsampling chroma channels of video content is a processing performed in most video distribution pipelines. In such pipelines, chroma subsampled content also undergoes video coding, using a codec, before being distributed. Presently, it is common practice to optimize compression efficiency and visual quality of content with respect to its subsampled version rather than the original one. Although this may suffice for traditional imagery, it is unclear if emerging technologies such as Wide Color Gamut and High Dynamic Range are more sensitive to quality loss due to chroma subsampling. In this article, we assess the efficiency of chroma subsampling when compressing HDR content. We compare the performance of two different downsampling filters against the full chroma sampling. Objective results show that distributing 4:4:4 is more efficient than its 4:2:0 counterpart at medium to high bit-rates. For low bit-rates, this increase in efficiency is reduced and in some cases even reversed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".