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JACIII Vol.20 No.2 pp. 205-211
doi: 10.20965/jaciii.2016.p0205
(2016)

Paper:

Objective Image Quality Assessment Based on Saliency Map

Longsheng Wei*,**, Wei Liu*,**, Xinmei Wang*, Feng Liu*, and Dapeng Luo***

*School of Automation, China University of Geosciences
Wuhan 430074, China
**Computational Intelligence & Information Processing Laboratory, China University of Geosciences
Wuhan 430074, China
***Faculty of Mechanical and Electronic Information, China University of Geosciences
Wuhan 430074, China

Received:
November 10, 2015
Accepted:
December 10, 2015
Online released:
March 18, 2016
Published:
March 20, 2016
Keywords:
image quality assessment, objective analysis, image retargeting, saliency map
Abstract

The development of objective image quality assessment metrics aligned with human perception is of fundamental importance to numerous image processing applications. In this paper, an objective image quality assessment approach based on saliency map is proposed. By local shift estimation method, the retargeted image is resized to the same size as the reference image. A gradient magnitude similarity map is computed by comparing the retargeted and reference images. The more similarly, the brighter of pixels in the gradient magnitude similarity map. At the same time, a saliency map of reference image is achieved by visual attention. Finally, an overall image quality score is computed from the gradient magnitude similarity map via saliency pooling strategy. The most important step in our approach is to generate a gradient magnitude similarity map that indicates at each spatial location in the source image how the structural information is preserved in the retargeted image. There are two key contributions in this paper, one is that we add the texture feature in computing saliency map because image gradient is very sensitive to texture information, and the other is that we propose a new objective image quality metrics by introducing saliency map into image quality evaluation. Experimental results indicate that the evaluation indexes of our approach are better than existing methods in the literature.

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Last updated on Mar. 28, 2017