Robust contourlet-based watermarking for depth-image-based rendering 3D images
Bibliographic record
Abstract
In this paper, we propose a new blind watermarking scheme for depth-image-based rendering (DIBR) 3D images. The center view and the depth map are available at the content provider side. After applying contourlet transform (CT) to the center view, we embed the watermark into the selected contourlet subbands of the center view by quantization on certain contourlet coefficients. The virtual left and right views are generated from the watermarked center view and the associated depth map using DIBR technique at the receiver side. The statistical differences between quantized and unquantized contourlet coefficients are used for watermark extraction. The watermark can be detected with a low bit error rate (BER) from the center view, the left and right views even when each view is distorted and distributed separately. The simulation results demonstrate that our scheme keeps good perceptual quality of the watermarked images under both objective and subjective image quality estimations. Moreover, compared with other related methods, the proposed scheme has better performance in terms of robustness against image compression, noise addition and geometric attacks such as rotation, scaling and cropping.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".