LAUSR

Human beings always evaluate the perceptual quality of an image coupled with identifying the semantic content of images. This paper addresses the correlation issue between stereoscopic image quality assessment (SIQA) and semantic recognition. In contrast to the previous SIQA methods that relied on binocular quality-aware features of a stereoscopic image, our approach also extracts binocular semantic features using a pre-trained deep convolutional neural network (DCNN) on a large dataset like ImageNet dataset, as well as the manually designed binocular quality-aware features. It can solve the problem of limited SIQA dataset size and facilitate better prediction on the quality. Experimental results demonstrate that the binocular semantic features are a good predictor for the stereoscopic image quality. The proposed method outperforms the state-of-the-art SIQA methods on four benchmark SIQA datasets. Significantly, all Spearman rank-order correlation coefficients (SROCCs) between the predicted scores and the subjective scores on the four datasets exceed 0.95. The MATLAB source code of the proposed method is available at https://github.com/kyohoonsim.