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No-reference stereoscopic image quality assessment using a multi-task CNN and registered distortion representation
Pattern Recognition ( IF 5.898 ) Pub Date : 2019-12-16 , DOI: 10.1016/j.patcog.2019.107168
Yiqing Shi; Wenzhong Guo; Yuzhen Niu; Jiamei Zhan

Scene discrepancy between the left and right views presents more challenges for image quality assessment (IQA) of stereoscopic images as opposed to monocular ones. Existing no-reference stereoscopic IQA (NR-SIQA) metrics cannot achieve a good performance on asymmetrically distorted stereoscopic images. In this paper, we propose an NR-SIQA index that first addresses scene discrepancy by means of image registration. It then uses a registered distortion representation based on the left and registered right views to represent the distortion in the stereoscopic image. Because different distortion types influence image quality differently, a multi-task convolutional neural network (CNN) is employed to learn image quality prediction and distortion-type identification simultaneously. We first design a one-column multi-task CNN model, that learns from the registered distortion representation. Then, we extend the one-column model to a three-column model, which also learns from the left and right views. Our experimental results validate the effectiveness of the proposed registered distortion representation and multi-task CNN architecture. The proposed one- and three-column models outperform the state-of-the-art NR-SIQA metrics, especially for asymmetrically distorted stereoscopic images.
更新日期:2020-01-04

 

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