On the analysis and design of variable rate trellis source codes
Bibliographic record
Abstract
We extend the fixed slope lossy algorithm derived from the kth order arithmetic codeword length function to the case of trellis structured decoders and, as a result, get a new coding method, namely, the so-called variable rate trellis source encoding which aims to jointly optimize the resulting distortion, compression rate, and selected encoding path. It is shown both theoretically and experimentally that properly designed variable rate trellis source codes are very efficient in low rate regions (below 0.8 bits/sample). With k=8 and the number of states in the decoder =32, the mean squared error encoding performance at the rate 1/2 bits/sample for memoryless Laplacian sources is about 1 dB better than that afforded by the trellis coded quantizers with 256 states. With k=8 and the number of states in the decoder =256, the mean squared error encoding performance at the rates of a fraction of 1 bit/sample for highly dependent Gauss Markov sources with correlation coefficient 0.9 is within about 0.6 dB of the distortion rate function. Note that at such low rates, predictive coders usually perform poorly.
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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 | 0.000 | 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".