Error resilient pre/post-filtering for DCT-based block coding systems
Why this work is in the frame
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Bibliographic record
Abstract
Block coding based on the discrete cosine transform (DCT) is very popular in image and video compression. Pre/post-filtering can be attached to a DCT-based block coding system to improve coding efficiency as well as to mitigate blocking artifacts. Previously designed pre/post-filters are optimized to maximize coding efficiency solely. For image and video communication over unreliable channels, those pre/post-filters are sensitive to transmission errors. This paper addresses the problem of designing pre/post-filters which are more error resilient. Reconstruction performance is measured by how low the average reconstruction error is, and how uniformly the reconstruction error is distributed. A family of pre/post-filters is designed to provide desired tradeoffs between coding efficiency and robustness to transmission errors. Experiments show that these filtering operators can achieve superior reconstruction performance without sacrificing much coding performance.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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