Optimizing DNN based quality assessment metric for image compression: A novel rate control method
Bibliographic record
Abstract
In the existing coding standards, rate control (RC) plays a critical role in optimally allocating bit-rates to each coding unit, for improving rate-distortion performance under the limited bandwidth. However, the existing RC methods are mainly based on traditional distortion metrics, which fail to take the advantage of the emerging DNN based image quality assessment (IQA) metrics. In this paper, we set up the first attempt to achieve IQA score based RC for image compression. Specifically, a novel visualization based score-distortion (VSD) model and ρ-slope model are proposed to explicitly establish the relationship between IQA score and bit-rates. Then, by solving optimal rate-distortion optimization based on the IQA score, we propose a novel RC method for the HEVC standard. The experimental results show that, given the target bit-rates, the proposed RC method can accurately control the bit-rates and generate the compressed images with higher IQA score and better perceptual quality. More importantly, the proposed RC method is evaluated to be effective over two DNN based IQA metrics and four image datasets, exhibiting the potential in practical use. The code is available at https://github.com/Ffangqy/IQA-RC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".