Perceptual-Shaping Comparison of DWT-Based Pixel-Wise Masking Model with DCT-Based Watson Model
Bibliographic record
Abstract
It is very important to perceptually shape the watermark signal before embedding it into a host image according to the characteristics of the HVS (human vision system) since watermark invisibility is a necessary requirement for a successful watermarking application. Two popular HVS models have been proposed to deal with this problem: DCT-based Watson model and DWT-based PWM (pixel-wise masking) model, which correspond to the DCT-based and DWT-based watermarking techniques, respectively. Even though there is a common belief that the PWM model is better than the Watson model, there have been no studies that compare these two approaches. This paper is devoted to such a comparison. Our results show that the believed superiority of the PWM model relative to the Watson model is not correct and the Watson model indeed outperforms the PWM model. We argue that more accurate masking strategies in the wavelet domain are needed for DWT-based watermarking applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".