Down-Sampling Design in DCT Domain With Arbitrary Ratio for Image/Video Transcoding
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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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".