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Blind Quality Assessment for DIBR-Synthesized Images Based on Chromatic and Disoccluded Information

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

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Abstract

In this paper, we propose a novel method for no reference image quality assessment. It evaluates the visual quality of the synthesized images by considering chromatic and disoccluded information. Since human is sensitive to chromatic information, the chromatic information is extracted which is represented by the features of saturation and hue. Specially, we calculate the first derivative of saturation and hue maps by using local binary pattern (LBP) algorithm and extract features from LBP maps. In addition, inspired by the characteristic of the human visual system (HVS) and the synthesized image-specific distortion type, the proposed method extracts disoccluded maps as weighting maps for LBP maps. The support vector regression (SVR) model is used to predict the visual quality of images by using the extracted features. Compared with 8 state-of-the-art no-reference methods for natural or synthesized images, the proposed method shows improved performance on IRCCyN/IVC DIBR and MCL-3D databases.

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Author information

Authors and Affiliations

  1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Mengna Ding, Yuming Fang, Yifan Zuo & Zuowen Tan

Authors
  1. Mengna Ding
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  2. Yuming Fang
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  3. Yifan Zuo
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  4. Zuowen Tan
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Corresponding author

Correspondence to Yuming Fang .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Ding, M., Fang, Y., Zuo, Y., Tan, Z. (2019). Blind Quality Assessment for DIBR-Synthesized Images Based on Chromatic and Disoccluded Information. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_53

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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