<title>Toward optimal rate control: a study of the impact of spatial resolution, frame rate, and quantization on subjective video quality and bit rate</title>
Bibliographic record
Abstract
Multi-dimensional rate control schemes, which jointly adjust two or three coding parameters, have been recently proposed to achieve a target bit rate while maximizing some objective measures of video quality. The objective measures used in these schemes are the peak signal-to-noise ratio (PSNR) or the sum of absolute errors (SAE) of the decoded video. These objective measures of quality may differ substantially from subjective quality, especially when changes of spatial resolution and frame rate are involved. The proposed schemes are, therefore, not optimal in terms of human visual perception. We have investigated the impact on subjective video quality of the three coding parameters: spatial resolution, frame rate, and quantization parameter (QP). To this end, we have conducted two experiments using the H.263+ codec and five video sequences. In Experiment 1, we evaluated the impact of jointly adjusting QP and frame rate on subjective quality and bit rate. In Experiment 2, we evaluated the impact of jointly adjusting QP and spatial resolution. From these experiments, we suggest several general rules and guidelines that can be useful in the design of an optimal multi-dimensional rate control scheme. The experiments also show that PSNR and SAE do not adequately reflect perceived video quality when changes in spatial resolution and frame rate are involved, and are therefore not adequate for assessing quality in a multi-dimensional rate control scheme. This paper describes the method and results of the investigation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".