End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks
Bibliographic record
Abstract
Blind video quality assessment (BVQA) algorithms are traditionally designed with a two-stage approach - a feature extraction stage that computes typically hand-crafted spatial and/or temporal features, and a regression stage working in the feature space that predicts the perceptual quality of the video. Unlike the traditional BVQA methods, we propose a Video Multi-task End-to-end Optimized neural Network (V-MEON) that merges the two stages into one, where the feature extractor and the regressor are jointly optimized. Our model uses a multi-task DNN framework that not only estimates the perceptual quality of the test video but also provides a probabilistic prediction of its codec type. This framework allows us to train the network with two complementary sets of labels, both of which can be obtained at low cost. The training process is composed of two steps. In the first step, early convolutional layers are pre-trained to extract spatiotemporal quality-related features with the codec classification subtask. In the second step, initialized with the pre-trained feature extractor, the whole network is jointly optimized with the two subtasks together. An additional critical step is the adoption of 3D convolutional layers, which creates novel spatiotemporal features that lead to a significant performance boost. Experimental results show that the proposed model clearly outperforms state-of-the-art BVQA methods.The source code of V-MEON is available at https://ece.uwaterloo.ca/~zduanmu/acmmm2018bvqa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".