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A No-Reference Stereoscopic Image Quality Assessment Based on Cartoon Texture Decomposition and Human Visual System

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Digital Multimedia Communications (IFTC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2067))

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Abstract

It has become important to develop an objective stereoscopic image quality assessment (SIQA) method that aligns with human visual system characteristics. To enable an accurate and efficient assessment of stereoscopic image quality, this study introduces a no-reference stereoscopic image quality assessment model, aiming to address the limitations of existing assessment methods. Considering that natural images typically contain information like textures and contours, we decompose stereoscopic views into cartoon and texture images to effectively extract monocular perception features. We also take binocular difference information to explain binocular perception features. Subsequently, a CNN multi-branch architecture is employed to feed images into the network for extracting relevant feature mappings. Finally, all sub-networks are used for quality scoring predictions, resulting in the final perceptual quality score. Experiments conducted on the LIVE dataset have demonstrated the superiority of this approach.

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Acknowledgments

This work is supported by Shenyang science and technology plan project under Grant 23–407-3–32, Liaoning Province Natural Science Foundation under Grant 2023-MS-139 and National Natural Science Foundation of China under Grant 61901205.

Author information

Authors and Affiliations

  1. College of Information, School of Cyber Science and Engineering, Liaoning University, Shenyang, Liaoning, China

    Yun Liu, Yan Bai, Yaohui Wang & Minzhu Jin

  2. Lianyungang Aids to Navigation Department of Donghai Navigation Safety Administration, Lianyungang, Jiangsu, China

    Bo Liu

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  1. Yun Liu
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  2. Yan Bai
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  3. Yaohui Wang
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  4. Minzhu Jin
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  5. Bo Liu
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Corresponding authors

Correspondence to Yaohui Wang , Minzhu Jin or Bo Liu .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong Univeristy, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Communication University of China, Beijing, China

    Long Ye

  4. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  5. Shanghai University, Shanghai, China

    Ping An

  6. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

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Liu, Y., Bai, Y., Wang, Y., Jin, M., Liu, B. (2024). A No-Reference Stereoscopic Image Quality Assessment Based on Cartoon Texture Decomposition and Human Visual System. In: Zhai, G., Zhou, J., Ye, L., Yang, H., An, P., Yang, X. (eds) Digital Multimedia Communications. IFTC 2023. Communications in Computer and Information Science, vol 2067. Springer, Singapore. https://doi.org/10.1007/978-981-97-3626-3_6

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