Color-Sensitivity-Based Combined PSNR for Objective Video Quality Assessment
Bibliographic record
Abstract
The peak signal-to-noise ratio (PSNR) has been widely employed as an objective video quality assessment (VQA) metric. Usually, videos are represented in the YCbCr color space, which results in three PSNR values for each video frame. Several VQA metrics have been proposed to measure the video quality with a single combined PSNR. However, these metrics are derived heuristically without theoretical justification. In this paper, based on our extensive subjective tests on the sensitivity of the human visual system to different color components, we derive the optimal weighting coefficients of a color-sensitivity-based combined PSNR (CSPSNR). Moreover, to verify the performance of the combined PSNR, test sequences with different levels of combined PSNRs are used to evaluate the quality of the videos. However, no such database is currently available for measuring the effectiveness of different methods regarding combined PSNRs. In this paper, we design a novel coding scheme to produce sequences whose PSNRs are the combinations of different levels of PSNRs of YCbCr, with which the correlation between the subjective score and the combined PSNR is analyzed. Experiment results and statistical analysis demonstrate that the proposed CSPSNR correlates better with the mean opinion score than the existing methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".