Bibliographic record
Abstract
The quality assessment of 3D images is playing a critical role in 3D multimedia applications. In this paper, we propose a novel watermarking based quality assessment scheme for depth-image-based rendering (DIBR) 3D images. The scheme utilizes the extracted watermarks to evaluate the quality of the watermarked images under various distortions. In this scheme, the watermark is embedded into the selected dual-tree complex wavelet transform (DT-CWT) coefficients of the center view. The watermark can be detected from the watermarked center view, the synthesized left and right views separately, and the quality of these views under various attacks can be estimated by the extracted watermark degradation. The performance of the proposed scheme is evaluated in terms of both the classical 2D quality metrics and some well-known 3D quality models. The estimated quality and the calculated quality are highly correlated, which demonstrates that the proposed scheme can assess the quality of DIBR 3D images with high accuracy under JPEG compression, JPEG2000 compression, Gaussian noise and Gaussian blur.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".