No-reference video quality assessment using data dimensionality reduction and attention-based pooling

This paper proposes a new end-to-end no-reference (NR) video quality assessment (VQA) algorithm that makes use of dimensionality reduction and attention-based pooling. Firstly, the dataset is expanded through data enhancement based on frame sampling. Secondly, the cropped video blocks are input into the trainable data dimensionality reduction module which adopts 3D convolution to reduce the dimension of the data. Then, the dimensionality reduced data is input into the backbone of the algorithm to extract spatial features. The extracted features are pooled through attention-based pooling. Finally, the pooled features are regressed to the quality score through the full connection layer. Experimental results show that the proposed algorithm has achieved competitive performance on the LIVE, LIVE Mobile and CVD2014 datasets, and has low complexity.