Image Processing. A new Approach via Informational Entropy and Informational Divergence of non Random Functions
Why this work is in the frame
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Bibliographic record
Abstract
By combining a maximum conditional entropy principle with a basic equation of (Shannon) information theory, one can obtain a meaningful concept of informational entropy of non random functions. When this entropy is applied to the brightness function of an image, one so has at hand a new tool which provides new approaches to some image processing problems, such as, for instance, image representation, image compression and image similarity. As a by-product, to some extent, this new modelling provides a support to the so-called monkey model of image entropy. But while the latter involves the brightness itself, here, the entropy of the brightness function is expressed in terms of the contrast of the brightness instead of the brightness itself. In this framework, a new concept of informational divergence of an image is obtained, which could be of help in image analysis.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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