A Two-Stage Algorithm to Reduce Encoding Delay of Turbo Source Coding
Bibliographic record
Abstract
Lossless turbo source coding employs an iterative encoding algorithm to search for the smallest codeword length that guarantees zero distortion. Although such encoder achieves promising compression rates, running the iterative algorithm for each individual message block imposes a large delay on the system. To reduce this delay, we propose a two-stage encoding algorithm for turbo source coding. We show that converging to zero distortion after a definite number of iterations, can be predicted from the earlier behavior of the distortion function. This will enable us to produce a quick, and yet sufficiently accurate, estimate of the codeword length in the first encoding stage. In the second stage, we iteratively increase this estimated codeword length until reaching zero distortion. Also, we show that employing an auxiliary distortion measure at the first stage of encoding may allow for better estimates and decrease the delay furthermore. Numerical results show that the proposed algorithm will decrease the encoding delay up to 19%. Although there are previous works in the literature on delay reduction of turbo source coding, those works achieve lower delays by reducing the message block length. However, the proposed algorithm achieves lower delays for the same block length and therefore the actual "per bit" encoding delay is decreased.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".