Efficient Algorithm for Globally Optimal Uneven Erasure-Protected Packetization of Scalable Code Streams
Bibliographic record
Abstract
A new algorithm is presented for rate-fidelity optimal packetization of scalable source bit streams with uneven erasure protection. It provides the globally optimal solution for input sources of convex rate-fidelity function and for a wide class of erasure channels, including channels for which the probability of losing n packets is monotonically decreasing in n, and independent erasure channels with packet erasure rate smaller than 0.5. The time and space complexities of the new algorithm are both O(NL), where N is the number of packets and L is the packet payload size, comparing to the O(NL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sup> ) time and space complexities of the existing globally optimal solution. When applied to SPIHT compressed images, the results of the proposed algorithm are virtually the same as the globally optima
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".