No-Reference Video Quality Assessment Using Transformers and Attention Recurrent Networks
Bibliographic record
Abstract
In recent years, numerous studies have investigated the development of methods for video quality assessment (VQA). These studies have predominantly focused on specific types of video degradation tailored to the application of interest. However, natural videos or recent videos generated by users (UGC) present complex distortions that are not easy to model. Consequently, most current VQA approaches struggle to achieve high performance when applied to these videos. In this paper, we propose a novel Transformer-based architecture that extracts spatial distortion features and spatio-temporal features from videos in two specialized branches. The spatial distortion branch leverages a transfer learning strategy where a standard ViT is pre-trained using a masked autoencoder (MAE) self-supervised learning task, and then fine-tuned to predict the distortion type of corrupted images from the CSIQ database. The features from this branch capture degradation at the level of individual frames. On the other hand, the second branch employs a 3D Shifted Windows Transformer (Swin-T) to extract spatio-temporal features across multiple frames. Once again, we use transfer learning to extract rich features by pre-training this 3D Swin-T model on a video dataset for human action recognition. Finally, a temporal memory block hinged on an attention recurrent neural networks is proposed to predict the final video quality score from the spatio-temporal sequence of features. We evaluate the performance of our method on two popular UGC databases, namely KoNViD-1k and LIVE-VQC. Results show it outperforms state-of-the-art models on the KoNViD-1k database, achieving a SROCC performance of 0.927 and a PLCC of 0.925, while also delivering highly competitive results on the LIVE-VQC database.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".