Efficient Rate–Distortion Optimal Packetization of Embedded Bitstreams Into Independent Source Packets
Bibliographic record
Abstract
This paper addresses the rate-distortion (R-D) optimal packetization (RDOP) of embedded bitstreams into independent source packets, in order to limit error propagation in transmission of images over packet noisy channels. The input embedded stream is assumed to be an interleaving of K independently decodable basic streams. To form independent source packets, the set of basic streams is partitioned into N groups. The streams within each group are then interleaved to generate a source packet. Error/erasure protection may be further applied along/across source packets, to produce the channel packets to be transmitted. The RDOP problem previously formulated by Wu et at. has the goal of finding the partitioning that minimizes the distortion when all source packets are decoded. We extend the problem formulation such that to also include the minimization of the expected distortion for general transmission scenarios that may apply uneven erasure/ error protection. Further, we show that the dynamic programming (DP) algorithm of Wu et al. can be extended to solve the general RDOP problem. The main contribution of this paper is a fast divide-and-conquer algorithm (D&C) to find the globally optimal solution, under the assumption that all basic streams have convex R-D curves. Instrumental in obtaining the fast solution is our result which proves that the problem can be formulated as a series of matrix search problems in totally monotone matrices. The proposed D&C reduces the running time from O(K(2)LN) where L is the size of each packet achieved by the DP solution to O(NKL log K). Experiments on SPIHT coded images demonstrate that the speedup is significant 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".