Filter Banks for Prediction-Compensated Multiple Description Coding
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the design and application of the optimal filter banks for a prediction-compensated multiple description coding (PC-MDC) scheme, where the coefficients in each subband are split into two descriptions. Each description also includes the prediction residuals of the data in the other description. The optimal designs of orthogonal and biorthogonal filter banks with multiple-level decompositions are formulated in a unified framework. The optimal results in all cases are found to be very close to the optimal filter banks in traditional single description coding. This allows us to apply the proposed method to existing systems with single-description-optimized filter banks and still enjoy near-optimal performance. Image coding results in the JPEG 2000 framework show that the proposed method achieves similar or better performance than other methods. It also has lower complexity and is more compatible to the JPEG 2000 standard.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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