Int. J. of Granular Computing, Rough Sets and Intelligent Systems   »   2013 Vol.3, No.1

 

 

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Title: RCBM: a rough content-based image quality assessment metric

 

Authors: Qian Yu; Wei Dong; Chang N. Zhang

 

Addresses:
Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada
Texas Instruments, 12500, TI Blvd., Dallas, Texas, 75243, USA
Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

 

Abstract: A generalised content-based image quality assessment technique is proposed in this paper. Different from many existing image quality metrics, where the digital image quality is evaluated by comparing with a reference image and a single 'exact' value is provided for the purpose of 'accurately' quantifying the image quality, our proposed method defines the image quality metric based on the theory of the rough fuzzy integral and a region (with a pair of the boundary values) is presented to estimate the image quality instead of a scalar value. The new philosophy for the proposed 'rough' content-based image quality metric lays on the acceptance of the uncertainty of subjective image quality assessment through the human visual system (HVS) and addresses this kind of uncertainty by applying the rigorous mathematical concept of the rough and fuzzy set on the standard content-based image quality analysis. Therefore, the proposed method is a good mimicking of the subjective image quality assessment and meets the accuracy requirement under the uncertainty measurement of HVS.

 

Keywords: rough sets; fuzzy measures; rough fuzzy integral; image quality assessment; structural similarity; content-based image assessment; uncertainty measurement; subjective assessment; human visual system.

 

DOI: 10.1504/IJGCRSIS.2013.054126

 

Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2013 Vol.3, No.1, pp.44 - 58

 

Date of acceptance: 01 Feb 2013
Available online: 22 May 2013

 

 

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