Visual color difference evaluation of standard color pixel representations for high dynamic range video compression
Bibliographic record
Abstract
With the recent introduction of High Dynamic Range (HDR) and Wide Color Gamut (WCG) technologies, viewers' quality of experience is highly enriched. To distribute HDR videos over a transmission pipeline, color pixels need to be quantized into integer code-words. Linear quantization is not optimal since the Human Visual System (HVS) do not perceive light in a linear fashion. Thus, perceptual transfer functions (PTFs) and color pixel representations are used to convert linear light and color values into a non-linear domain, so that they correspond more closely to the response of the human eye. In this work, we measure the visual color differences caused by different PTFs and color representation with 10-bit quantization. Our study encompasses all the visible colors of the BT.2020 gamut at different representative luminance levels. Visual color differences are predicted using a perceptual color error metric (CIE ΔE2000). Results show that visible color distortion can already occur before any type of video compression is performed on the signal and that choosing the right PTF and color representation can greatly reduce these distortions and effectively enhance the quality of experience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".