No-Reference Video Quality Assessment with 3D Shearlet Transform and Convolutional Neural Networks

In this work we propose an efficient general-purpose no-reference (NR) video quality assessment (VQA) framework which is based on 3D shearlet transform and Convolutional Neural Network (CNN). Taking video blocks as input, simple and efficient primary spatiotemporal features are extracted by 3D shearlet transform, which are capable of capturing the Natural Scene Statistics (NSS) properties. Then, CNN and logistic regression are concatenated to exaggerate the discriminative parts of the primary features and predict a perceptual quality score. The resulting algorithm, which we name SACONVA (SheArlet and COnvolutional neural network based No-reference Video quality Assessment), is tested on well-known VQA databases of LIVE, IVPL and CSIQ. The testing results have demonstrated SACONVA performs well in predicting video quality and is competitive with current state-of-the-art full-reference VQA methods and general-purpose NR-VQA algorithms. Besides, SACONVA is extended to classify different video distortion types in these three databases and achieves excellent classification accuracy. In addition, we also demonstrate that SACONVA can be directly applied in real applications such as blind video denoising.