Image Quality Assessment Based on Improved Structural SIMilarity

  • Jinjian Wu
  • Fei Qi
  • Guangming Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7674)


In this paper, we propose a novel image quality assessment (IQA) based on an Improved Structural SIMilarity (ISSIM) which considers the spatial distributions of image structures. The existing structural similarity (SSIM) metric, which measures structure loss based on statistical moments, i.e., the mean and variance, represents mainly the luminance change of pixels rather than describing the spatial distribution. However, the human visual system (HVS) is highly adapted to extract structures with regular spatial distributions. In this paper, we employ a self-similarity based procedure to describe the spatial distribution of image structures. Then, combining with the statistical characters, we improve the structural similarity based quality metric. Furthermore, considering the viewing condition, we extend the ISSIM metric to the multi-scale space. Experimental results demonstrate the proposed IQA metric is more consistent with the human perception than the SSIM metric.


Image Quality Assessment Structural Similarity Statistical Character Spatial distribution Self-Similarity 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jinjian Wu
    • 1
  • Fei Qi
    • 1
  • Guangming Shi
    • 1
  1. 1.School of Electronic EngineeringXidian UniversityXi’anP.R. China

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