Efficient No-Reference Video Quality Assessment Using Video Masked Autoencoder Feature Mixing
Bibliographic record
Abstract
Video quality assessment (VQA) is a key component in video processing and analysis, particularly with the growing volume of user-generated content (UGC) uploaded to platforms like YouTube and social media. In this work, we present a lightweight No-Reference Video Quality Assessment (NR-VQA), which combines a Video Masked Autoencoder (VMAE) and a modified Multilayer Perceptron Mixer (MixVPR) for quality prediction. The proposed VMAE and MLP-Mixer are based on a combination of a regressor inspired by the MixVPR feature mixer. The experimental results show that the proposed method achieves an excellent trade-off between performance and complexity, demonstrating its suitability for real-world applications. The algorithm’s performance is tested across five UGC datasets: KoNViD-1k and LIVE VQC, LIVE QUalcomm, CVD2014, and YouTube UGC. This work builds on advances in deep learning, particularly in transformer-based architectures and feature-mixing techniques.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".