Blind Video Quality Assessment Using Fusion of Novel Structural Features and Deep Features

Abstract

AbstractWe propose a robust and efficient blind video quality assessment model using fusion of novel structural features and deep semantic features. As the human visual system (HVS) is very sensitive to the structural contents in a visual scene, we come up with a novel structural feature extractor that uses a two-level encoding scheme. In addition, we employ a pre-trained Convolutional Neural Network (CNN) model Inception-v3 that extracts semantic features from the sampled video frames. Further, structural and deep semantic features are concatenated and applied to a support vector regression (SVR) that predicts the final visual quality scores of the videos. The performance of the proposed method is validated on three popular and widely used authentic distortions datasets, LIVE-VQC, KoNViD-1k, and LIVE Qualcomm. Results show excellent performance of the proposed model compared with other state-of-the-art methods with significantly reduced computational burden.KeywordsStructural featuresSupport vector regressionConvolutional Neural Network