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Blind Image Quality Assessment Based on Local Quantized Pattern

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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

No-reference (NR) image quality assessment (IQA) metrics have attracted great attention in the area of image processing. Since there is no access to the reference images, the generic NR IQA metrics have made less progress than the full-reference and reduced-reference IQA metrics. In this paper, we aim to propose an effective quality-aware feature based on the local quantized pattern (LQP) for quality evaluation. Firstly, a codebook is learned by K-means clustering the LQP descriptors of a corpus of pristine images. Based on the codebook, the LQP descriptors of images are then encoded to derive the quality-aware features. Finally, the image features are mapped to the subjective quality scores using the support vector regression. Experimental results on several public databases indicate the propose method performs highly consistent with the human visual perception.

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Acknowledgement

This work was supported by the Major State Basic Research Development Program of China (973 Program, No. 2013CB329402), the National Natural Science Foundation of China (Nos. 61401325, 61472301, 61301288, 61227004), the Research Fund for the Doctoral Program of Higher Education (No. 20130203130001), International cooperation project of Shaanxi science and technology R&D program (No. 2014KW01-02).

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, China

    Yazhong Zhang, Jinjian Wu, Xuemei Xie & Guangming Shi

Authors
  1. Yazhong Zhang
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  2. Jinjian Wu
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  3. Xuemei Xie
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  4. Guangming Shi
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Corresponding author

Correspondence to Xuemei Xie .

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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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Zhang, Y., Wu, J., Xie, X., Shi, G. (2016). Blind Image Quality Assessment Based on Local Quantized Pattern. 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_24

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  • DOI: https://doi.org/10.1007/978-3-319-48896-7_24

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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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