Encoding-Aware Deep Video Super-Resolution Framework
Bibliographic record
Abstract
Video super-resolution(VSR) upscales a low-resolution video to the higher one. Most applications require compression of the super-resolved video due to limited internet bandwidth and storage capacity. However, most studies on VSR techniques have focused only on improving image quality, ignoring the impact of the compression process on visual quality. Consequently, even a VSR with good visual quality has a risk of significant loss of quality when serviced online or stored as a file. To address this problem, we propose an encoding-aware VSR framework. In the framework, we created a differentiable virtual codec to estimate the bit rate and used it for the loss function, which optimizes the super-resolved videos by considering the rate-distortion trade-off relationship and eventually leads to the prevention of visual quality degradation. According to the results, our real-time VSR model for x4 upscaling, trained with 1,191K parameters, yields a maximum gain of 13.2% over state-of-the-art VSR models based on the Bjøntegaard delta rate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".