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Image quality assessment based on regions of interest

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Abstract

Most methods in the literature of image quality assessment (IQA) use whole image information for measuring image quality. However, human perception does not always use this criterion to assess the quality of images. Individuals usually provide their opinions by considering only some parts of an image, called regions of interest. Based on this hypothesis, in this research work, a segmentation technique is initially employed to obtain a bi-level image map composed of the foreground and background information. A patch selection strategy is then proposed to choose some particular patches based on the foreground information as the regions of interest for IQA. Three recent IQA methods in the literature are considered to demonstrate the improvement in IQA when using only the extracted regions of interest. To evaluate the impact of the proposed patch selection strategy in various IQA metrics, three publicly available datasets were used for experiments. Experimental results have revealed that our proposal, based on the regions of interest, can improve quality measures of three IQA methods.

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Authors and Affiliations

  1. Laboratoire d’Informatique (LI EA6300), Université Francois-Rabelais, Tours, France

    A. Alaei, R. Raveaux & D. Conte

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  1. A. Alaei
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  2. R. Raveaux
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  3. D. Conte
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Correspondence to A. Alaei.

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Alaei, A., Raveaux, R. & Conte, D. Image quality assessment based on regions of interest. SIViP 11, 673–680 (2017). https://doi.org/10.1007/s11760-016-1009-z

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  • DOI: https://doi.org/10.1007/s11760-016-1009-z

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