Estimating just‐noticeable distortion for images/videos in pixel domain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing pixel‐based just noticeable distortion (JND) models only take into account luminance adaptation and texture masking (TM). Similarly, existing discrete cosine transform (DCT) based models do not take into account foveal vision effects and do not estimate TM efficiently. As human visual system (HVS) is not sensitive to distortion below the JND threshold, estimation of the perceptual visibility threshold is widely used in digital and video processing applications. The authors propose a comprehensive and efficient pixel‐based JND model incorporating all major factors which contribute to the JND estimation. The evaluation of contrast masking (CM) is done by distinguishing the edge and TM with respect to the entropy masking properties of the HVS. Similarly, the foveal vision effects are also taken into account for the comprehensive estimation of contrast sensitivity function (CSF). Hence, the proposed pixel‐based JND model incorporates the spatio‐temporal CSF, foveal vision effects, influence of eye‐movement, luminance adaptation and CM to be more consistent with human perception. The incorporation of these important factors makes the proposed model the most comprehensive and efficient in the current literature. Psychophysical experiments were performed to test the proposed model. The results show the proposed model comprehensively outperforms other existing models proving its efficiency.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| 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