Saliency-Guided Just Noticeable Distortion Estimation Using the Normalized Laplacian Pyramid
Bibliographic record
Abstract
The human visual system (HVS), like any other physical system, has limitations. For instance, it is known that the HVS can only sense the content changes that are larger than the so-called just noticeable distortion (JND) threshold. Also, to reduce the computational load on the brain, the visual attention mechanism is deployed such that regions with higher visual saliency are processed with higher priority than other less-salient regions. It is also known that visual saliency has a modulatory effect on JND thresholds. In this letter, we present a novel pixel-wise JND estimation method that considers the interplay between visual saliency and JND thresholds. In the proposed method, the largest JND thresholds of a given image are found such that the perceptual distance between the image and its JND noise-contaminated version is minimized in a perceptual space defined by the coefficients of the image in a normalized Laplacian pyramid. Experimental results indicate that the proposed method outperforms four of the latest JND models for static images.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".