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Image Quality Assessment Based on Inter-Patch and Intra-Patch Similarity

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  • Fei Zhou
  • Zongqing Lu
  • Can Wang
  • Wen Sun
  • Shu-Tao Xia
  • Qingmin Liao

Abstract

In this paper, we propose a full-reference (FR) image quality assessment (IQA) scheme, which evaluates image fidelity from two aspects: the inter-patch similarity and the intra-patch similarity. The scheme is performed in a patch-wise fashion so that a quality map can be obtained. On one hand, we investigate the disparity between one image patch and its adjacent ones. This disparity is visually described by an inter-patch feature, where the hybrid effect of luminance masking and contrast masking is taken into account. The inter-patch similarity is further measured by modifying the normalized correlation coefficient (NCC). On the other hand, we also attach importance to the impact of image contents within one patch on the IQA problem. For the intra-patch feature, we consider image curvature as an important complement of image gradient. According to local image contents, the intra-patch similarity is measured by adaptively comparing image curvature and gradient. Besides, a nonlinear integration of the inter-patch and intra-patch similarity is presented to obtain an overall score of image quality. The experiments conducted on six publicly available image databases show that our scheme achieves better performance in comparison with several state-of-the-art schemes.

Suggested Citation

  • Fei Zhou & Zongqing Lu & Can Wang & Wen Sun & Shu-Tao Xia & Qingmin Liao, 2015. "Image Quality Assessment Based on Inter-Patch and Intra-Patch Similarity," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    • Handle: RePEc:plo:pone00:0116312
    DOI: 10.1371/journal.pone.0116312
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    References listed on IDEAS

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    1. Tao Yuan & Xinqi Zheng & Xuan Hu & Wei Zhou & Wei Wang, 2014. "A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-7, January.
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    Cited by:

    1. Jiachen Yang & Yancong Lin & Zhiqun Gao & Zhihan Lv & Wei Wei & Houbing Song, 2015. "Quality Index for Stereoscopic Images by Separately Evaluating Adding and Subtracting," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-19, December.
    2. Mariusz Oszust, 2016. "Full-Reference Image Quality Assessment with Linear Combination of Genetically Selected Quality Measures," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.

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