A perceptually lossless, model-based, texture compression technique
Why this work is in the frame
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Bibliographic record
Abstract
In natural scenes, still images as well as sequences, backgrounds, and objects' surfaces usually have a textural structure. Therefore, in order to efficiently code images it is crucial to investigate the texture compression problem. In this paper, a perceptually lossless, synthesis-by-analysis texture coding method is presented. The proposed approach is model based; the parameters of the model consist of a binary excitation signal and the parsimonious representation of the reconstruction filter. The estimated parameters, which allow to one synthesize, at the decoder site, a texture that is perceptually indistinguishable from the original one, are then compressed using a lossless strategy, which is based on a fast binary wavelet transformation specifically tailored to binary images. The proposed method leads to very good perceptual results superior to those of existing techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it