Rate control for improved picture quality in low-bit-rate video coding
Bibliographic record
Abstract
In low bit rate coding applications, high quantization levels might be needed to achieve a target bit rate. However, such high levels of quantization are likely to decrease picture quality. A possible solution is to reduce temporal resolution by dropping, for instance, selected frames thereby lessening the requirement for high quantization levels and thus improving video quality. Similarly, the spatial resolution of the encoded video could also be manipulated to achieve the target bit rate. Therefore, it might be possible to maximize picture quality by adjusting dynamically these three parameters while still meeting bit rate constraints. To do so effectively, the relationship between these parameters, alone or in combination, and subjective picture quality must be known. In this paper, we investigated the effect on subjective quality of: quantization alone (Experiment 1); a reduction in spatial resolution either alone or combined to moderate levels of quantization (Experiment 2); and a reduction of temporal resolution either alone or combined with moderate levels of quantization (Experiment 3). The results suggest that at very low bit rates reductions in spatial or temporal resolution combined with moderate levels of quantization might be an effective means of reducing bit rate without further loss in video quality.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".