Non-local pairwise energy-based model for the high-dynamic-range image compression problem
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Bibliographic record
Abstract
We present a new energy-based compression model for the display of high dynamic range images. The proposed tone mapping method tends to exploit the biologically inspired dynamic retina concept, which is herein mathematically expressed via an image representation based on the specification of the statistical distributions of the nonlocal gradient magnitude. In this framework, which also operates the notion of nonlocal gradient recently put forward by Gilboa and Osher, the detail-preserving contrast reduction problem is therefore expressed by a energy-based model with nonlocal pairwise pixel interactions defined on a complete graph whose cost function is locally minimized by a conjugate gradient descent procedure. The experiments demonstrate that the proposed compression method is efficient and provides pleasing results on various images with different scene contents and performs competitively compared to the best existing state-of-the-art tone mapping methods recently proposed in the literature.
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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 | 0.002 |
| 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