Context-dependent multilevel pattern matching for lossless image compression
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Bibliographic record
Abstract
In this paper, the multilevel pattern matching (MPM) code for compression of one-dimensional (1D) sequences is first generalized to compress two-dimensional (2D) images, resulting in a 2D multilevel pattern matching (MPM) code. It is shown that among all images of n pixels, the worst case redundancy of the 2D MPM code against any finite-template-based arithmetic code is O(1//spl radic/logn). This result contrasts unfavorably with the fact that among all 1D sequences of length n, the MPM code has a worst case redundancy of O(1/logn) against any finite-state arithmetic code; this is caused by the so-called 2D boundary effect. To alleviate the 2D boundary effect, we extend the 2D MPM code to the case of context modeling, yielding a context-dependent 2D MPM code. It is shown that among all images of n pixels, the context-dependent 2D MPM code has an O(1/logn) worst case redundancy against any finite-template-based arithmetic code satisfying a mild condition; this redundancy is better than that of the 2D MPM code without context models. Experimental results demonstrate that the context-dependent 2D MPM code significantly outperforms the 2D MPM code without context models for bi-level images. It is also demonstrated that, in terms of compression rates, the context-dependent 2D MPM code performs significantly better than the progressive coding mode of JBIG1 for both textual and bi-level images, and better than or comparably to the sequential coding mode of JBIG1 and JBIG2. In addition to its excellent compression performance, the context-dependent 2D MPM code allows progressive transmission of images.
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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 | 0.003 |
| 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 it