Down-Sampling Design in DCT Domain With Arbitrary Ratio for Image/Video Transcoding
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a designing framework for down-sampling compressed images/video with arbitrary ratio in the discrete cosine transform (DCT) domain. In this framework, we first derive a set of DCT-domain down-sampling methods which can be represented by a linear transform with double-sided matrix multiplication (LTDS) in the DCT domain and show that the set contains a wide range of methods with various complexity and visual quality. Then, for a preselected spatial-domain down-sampling method, we formulate an optimization problem for finding an LTDS to approximate the given spatial-domain down-sampling method for a trade-off between the visual quality and the complexity. By modeling LTDS as a multiple layer network, a so-called structural learning with forgetting algorithm is then applied to solve the optimization problem. The proposed framework has been applied to discover optimal LTDSs corresponding to a spatial down-sampling method with Butterworth low-pass filtering and bicubic interpolation. Experimental results show that the resulting LTDS achieves a significant reduction on the complexity when compared with other methods in the literature with similar 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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