Visual-saliency-enhanced image quality assessment indices

Author

Lin, J.Y. ; Tsung Jung Liu ; Weisi Lin ; Kuo, C.-C Jay

Author_Institution

Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA

fYear

2013

fDate

Oct. 29 2013-Nov. 1 2013

Firstpage

1

Lastpage

4

Abstract

Modern image quality assessment (IQA) indices, e.g. SSIM and FSIM, are proved to be effective for some image distortion types. However, they do not exploit the characteristics of the human visual system (HVS) explicitly. In this work, we investigate a method to incorporate the human visual saliency (VS) model in these full-reference indices, and call the resulting indices SSIM_VS and FSIM_VS, respectively. First, we decompose an image into non-overlapping patches, calculate visual saliency, and assign a parameter ranging from 0 and 1 to each patch. Then, the local SSIM or FSIM values of the patches are weighed by the said parameter. Finally, the weighed similarity of all patches are integrated into one single index for the whole image. Experimental results are given to demonstrate the improved performance of the proposed VS-enhanced indices.

Keywords

image enhancement; mean square error methods; FSIM index; IQA; SSIM index; full-reference indices; human visual saliency model; image decomposition; image distortion types; local FSIM values; local SSIM values; mean-squared-errors index; nonoverlapping patches; visual-saliency-enhanced image quality assessment indices; Boats; Correlation; Feature extraction; Image quality; Indexes; Quality assessment; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific

Conference_Location

Kaohsiung

Type

conf

DOI

10.1109/APSIPA.2013.6694328

Filename

6694328