Neural Multiple Description Image Coding with Semantic Polarization for Lossy Channels
Bibliographic record
Abstract
Multiple description coding (MDC) is a type of error-resilient source coding that is advantageous for communications over channels with high loss and long delays. While neural network-based MDC promises higher compression efficiency and better semantic awareness, the aspect of semantic awareness is under-investigated. This paper is among the first efforts to study MDCs with competing performance goals known as the distortion-classification tradeoff. A new design concept called semantic polarization is proposed to contradict the traditional philosophy of balanced side encoder designs. We present a simple conceptual model to demonstrate the advantage of polarized MDC in having higher probabilities of satisfying at least one performance goal. We also propose a detailed implementation of polarized MDC based on SRGANs. Experiments on public datasets show that compared with state-of-the-art single description neural image codecs, the proposed MDC has multiple benefits in terms of enlarged rate range, superior semantic protection, and better perception quality, at the cost of slightly reduced but competitive distortion performance.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".