A Distortion Optimal Rate Allocation Algorithm for Transmission of Embedded Bitstreams over Noisy Channels
Bibliographic record
Abstract
In this paper, a globally distortion optimal solution is proposed for FPP. The method, which is applicable to any source with arbitrary distortion-rate characteristics, has quadratic complexity in N, where N is the number of transmitted packets. It is based on constructing a search trellis in which trellis levels represent the number of packets, trellis states at a given level i embody the possible source rates corresponding to i packets, and edges represent different code rates. Such a trellis, starts from a single root state and spreads out in N levels. We prove that the backward application of a Viterbi-like algorithm to this trellis starting from the final states and working towards the root results in a survivor path that provides us with the distortion optimal rate allocation solution. The proposed method can also be applied to VPP, providing an alternative to the algorithm of V. Chande and N. Farvardin (2000) with comparable complexity
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".