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No-Reference Quality Assessment of Camera-Captured Distortion Images

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Advances in Multimedia Information Processing - PCM 2016 (PCM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9917))

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Abstract

In this paper we address the problem of quality assessment of camera images using three types of features. The first type of features measures the naturalness of an image, inspired by a recent finding that there exists high correlation between structural degradation and free energy entropy on natural scene images and this regulation will be gradually devastated as more distortions are introduced. The second type of features comes from an observation that a broad spectrum of statistics of distorted images can be caught by the generalized Gaussian distribution (GGD) according to natural scene statistics (NSS). These two groups of features are both based on NSS regulations, but they come from the considerations of local auto-regression and global histogram, respectively. The third type of features estimates the local sharpness by computing log-energies in the discrete wavelet transform domain. Finally our quality metric is achieved via an SVR-based machine learning tool and its performance is proved to be statistically better than state-of-the-art competitors on the CID2013 database, which is dedicated to the quality assessment of camera-captured images.

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Acknowledgment

This work is supported in part by the National Science Foundation of China (61379143), the Fundamental Research Funds for the Central Universities (2015QNA66, 2015XKMS032) and Science and Technology Planning Project of Nantong (BK2014022).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China

    Lijuan Tang, Leida Li, Jiansheng Qian & Jianying Zhang

  2. School of Information and Electrical Engineering, Jiangsu Vocational College of Business, Nantong, China

    Lijuan Tang

  3. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Ke Gu

Authors
  1. Lijuan Tang
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  2. Leida Li
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  3. Ke Gu
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  4. Jiansheng Qian
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  5. Jianying Zhang
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Correspondence to Lijuan Tang .

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Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Enqing Chen

  2. Jiaotong University, Xi’an, China

    Yihong Gong

  3. Zhengzhou University, Zhengzhou, China

    Yun Tie

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Tang, L., Li, L., Gu, K., Qian, J., Zhang, J. (2016). No-Reference Quality Assessment of Camera-Captured Distortion Images. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_58

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  • DOI: https://doi.org/10.1007/978-3-319-48896-7_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48895-0

  • Online ISBN: 978-3-319-48896-7

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