Communications in Information and Systems

Volume 23 (2023)

Number 4

Registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image

Pages: 423 – 445

DOI: https://dx.doi.org/10.4310/CIS.2023.v23.n4.a3

Authors

Yiqing Shi (College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China)

Wenzhong Guo (College of Computer and Data Science, Fuzhou University, Fuzhou, China)

Yuzhen Niu (College of Computer and Data Science, Fuzhou University, Fuzhou, China)

Yi Wu (College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China)

Abstract

Multiply-distorted stereoscopic images are common in real-world applications. The mixture of multiple distortions results in complex binocular visual behavior of multiply-distorted stereoscopic images, making it challenging for existing blind singly-distorted stereoscopic image quality assessment (IQA) methods to obtain satisfactory results on multiply-distorted stereoscopic images. Because binocular rivalry caused by different distortions in the left and right views greatly influences the final stereoscopic image quality, we propose a registration-based distortion and binocular representation for blind quality assessment of multiply-distorted stereoscopic image in this paper. First, we employ a registration-based distortion representation to characterize the distortion in the stereoscopic image. Then we represent the binocular rivalry by merging the left and right views into a cyclopean image. Considering that the color and intensity of pixels in the RGB image can better reflect the information of the distorted image, then a grayscale cyclopean image is further converted to the color binocular representation using tone mapping. Finally, a multiply-distorted stereoscopic IQA method based on a double-stream convolutional neural network is proposed. The two subnetworks are used to extract quality features from the registration-based distortion representation and color binocular representation, respectively. Experimental results demonstrate that the proposed model outperforms the state-of-the-art models on the multiply-distorted stereoscopic image databases.

Received 5 June 2023

Published 21 May 2024