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Image Quality Assessment Based on Natural Image Statistics

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Visual Quality Assessment for Natural and Medical Image

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Abstract

Since human visual system (HVS) is highly adapted to extract statistical information from the viewing scenes, extracting and mathematically modeling natural scene statistics (NSS) is a promising solution for image quality assessment (IQA), as an alteration for simulating HVS properties that is discussed in the previous chapter. Depending on how statistics information is modeled, in this chapter, we conclude and introduce several representative NSS-based types of methods. The first class of methods discussed in the chapter are based on the hypothesis underlying structural similarity, which assume the natural images are highly structured, and lower-quality images fail to have the similar structural information. Then, methods with local textural information extraction aiming at utilizing the statistical distribution changing with distort to measure distortion are introduced. Subsequently, the methods based on finding hidden independent components in nature images are presented. Finally, we put forward the methods that extract quality-aware features based on multifractal analysis, which capture the statistical complexity information of images in accordance with HVS. It is really worthy to point out that exploiting the image information jointly in different domains is necessary and constructive.

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    Yong Ding

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Ding, Y. (2018). Image Quality Assessment Based on Natural Image Statistics. In: Visual Quality Assessment for Natural and Medical Image. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56497-4_6

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  • DOI: https://doi.org/10.1007/978-3-662-56497-4_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-56495-0

  • Online ISBN: 978-3-662-56497-4

  • eBook Packages: EngineeringEngineering (R0)

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