Markov model aided decoding for image transmission using soft-decision-feedback
Bibliographic record
Abstract
Soft-decision-feedback MAP decoders are developed for joint source/channel decoding (JSCD) which uses the residual redundancy in two-dimensional sources. The source redundancy is described by a second order Markov model which is made available to the receiver for row-by-row decoding, wherein the output for one row is used to aid the decoding of the next row. Performance can be improved by generalizing so as to increase the vertical depth of the decoder. This is called sheet decoding, and entails generalizing trellis decoding of one-dimensional data to trellis decoding of two-dimensional data (2-D). The proposed soft-decision-feedback sheet decoder is based on the Bahl algorithm, and it is compared to a hard-decision-feedback sheet decoder which is based on the Viterbi algorithm. The method is applied to 3-bit DPCM picture transmission over a binary symmetric channel, and it is found that the soft-decision-feedback decoder with vertical depth V performs approximately as well as the hard-decision-feedback decoder with vertical depth V+1. Because the computational requirement of the decoders depends exponentially on the vertical depth, the soft-decision-feedbark decoder offers significant reduction in complexity. For standard monochrome Lena, at a channel bit error rate of 0.05, the V=1 and V=2 soft-decision-feedback decoder JSCD gains in RSNR are 5.0 and 6.3 dB, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".