Bibliographic record
Abstract
Effective and efficient objective video quality assessment (VQA) methods are highly desirable in modern visual communication systems for performance evaluation, quality control and resource allocation purposes. Simple VQA algorithms may be developed by direct extensions of still image quality assessment (IQA) approaches on a frame-by-frame basis. Advanced VQA methods take into account the temporal correlation and motion information contained in video signals but often lead to significantly increased computational complexity. Here we use a different approach to examine a video signal by considering it as a three-dimensional (3D) volume image. Specifically, we propose a 3D structural similarity (3D-SSIM) approach, which first creates a 3D quality map by applying SSIM evaluations within local 3D blocks, and then use local information content and local distortion based weighting methods to pool the quality map into a single quality measure. The resulting 3D-SSIM algorithm is computationally efficient and demonstrates highly competitive performance in comparison with state-of-the-art VQA algorithms when tested using four publicly available video quality databases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".