Fast R-D Optimal Packetization of Embedded Bitstreams into Independent Source Packets
Bibliographic record
Abstract
This work addresses the rate-distortion (R-D) optimal packetization (OP) of embedded bitstreams into independent source packets, in order to limit error propagation in transmission over packet lossy channels. The input embedded stream is assumed to be an interleaving of K independently decodable basic streams. To form N independent source packets, each of L symbols, the set of basic streams is partitioned into N groups. The objective of R-D OP is to find the partitioning which minimizes the distortion when all packets are decoded. We present a fast divide and conquer algorithm to find the globally optimal solution, under the assumption that all basic streams have convex R-D curves. The proposed algorithm reduces the running time from O(K2(L + N)) achieved by the existing dynamic programming solution, to O(NKL log K). Experiments on SPIHT coded images show that the speed up is much higher than predicted theoretically, thus rendering the R-D OP feasible in practice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".