GPU-aided real-time image/video super resolution based on error feedback
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
Super resolution is a process to generate high-resolution images from their low-resolution versions. In many applications such as super-HD (4K) TV, super resolution has to be performed in real time. In this paper we propose a real-time image/video super-resolution algorithm, which achieves good performance at low computational cost via off-line learning of interpolation errors in different pixel contexts. The proposed algorithm consists of three stages: fast edge-guided interpolation to generate an initial HR estimation, GPU-aided de-convolution, and error feedback compensation. All three stages can be implemented with GPU to support real-time applications. Experiments demonstrate the competitive performance of the new real-time super-resolution algorithm in both PSNR and visual quality.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it