Quality assessment of stereoscopic 3D images based on local and global visual characteristics
Bibliographic record
Abstract
The quality assessment of stereoscopic images is playing a critical role in 3D multimedia applications. The 3D image quality evaluation encounters many challenges and simple extension of the 2D quality metrics to the 3D case is not satisfying. In this work, we propose a new perceptual quality assessment scheme for stereoscopic 3D images by considering the human visual characteristics. After the log-Gabor filter processing, the local amplitude and phase from the left and right views of the reference and distorted 3D images are utilized as features in local quality evaluation. Meanwhile, the global structure changes of the left and right views are also incorporated into the final quality pooling. The overall 3D quality score is obtained by combining the local and global quality indexes together. The effectiveness of the designed metric is verified on three public 3D image quality assessment databases. Experimental results demonstrate that the proposed scheme exhibits better performance than other related algorithms in terms of consistency with subjective assessment of stereoscopic 3D images.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".