Novel UEP product code scheme with protograph-based linear permutation and iterative decoding for scalable image transmission
Bibliographic record
Abstract
This paper introduces a linear permutation module before the inner encoder of the iteratively decoded product coding structure, for the transmission of scalable bit streams over error-prone channels1. This can improve the error correction ability of the inner code when some source bits are known from the preceding outer code decoding stages. The product code consists of a protograph low-density parity-check code (inner code) and Reed-Solomon (RS) codes of various strengths (outer code). Further, an algorithm relying on protograph-based extrinsic information transfer analysis is devised to design good base matrices from which the linear permutations are constructed. In addition, an analytical formula for the expected fidelity of the reconstructed sequence is derived and utilized in the optimization of the RS codes redundancy assignment. The experimental results reveal that the proposed approach consistently outperforms the scheme without the linear permutation module, reaching peak improvements of 1.98 dB and 1.30 dB over binary symmetric channels (BSC) and additive white Gaussian noise (AWGN) channels, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".