Encoding-Aware Deep Video Super-Resolution Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it