A visual saliency modulated just noticeable distortion profile for image watermarking
Bibliographic record
Abstract
Previous perceptual watermarking schemes only partially used the results from human visual system (HVS) studies. The perceptual adjustment of the watermark is mainly based on different visual sensitivity models. Numerically, visual sensitivity can be regarded as the inverse of the just notice-able distortion (JND). Another aspect affecting human per-ception towards visual signal is visual attention which can enhance or reduce the actual visual sensitivity and conse-quently the JND profile needs to be adjusted. The technique described in this paper assists image watermarking by pro-ducing a visual saliency modulated JND profile that can be used as a guide to optimize image watermarking. Experi-mental results with subjective test confirm the improved per-formance of our visual saliency modulated JND profile for image watermarking. Our saliency modulated JND profile is capable of shaping lower injected-watermark energy onto more sensitive regions and higher energy onto the less per-ceptually significant regions in the image, which yields bet-ter visual quality of the watermarked image. 1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".