Paper Info
Title
Image Retargeting Quality Assessment
Abstract
Content-aware image retargeting is a technique that can flexibly display images with different aspect ratios and simultaneously preserve salient regions in images. Recently many image retargeting techniques have been proposed. To compare image quality by different retargeting methods fast and reliably, an objective metric simulating the human vision system (HVS) is presented in this paper. Different from traditional objective assessment methods that work in bottom-up manner (i.e., assembling pixel-level features in a local-to-global way), in this paper we propose to use a reverse order (top-down manner) that organizes image features from global to local viewpoints, leading to a new objective assessment metric for retargeted images. A scale-space matching method is designed to facilitate extraction of global geometric structures from retargeted images. By traversing the scale space from coarse to fine levels, local pixel correspondence is also established. The objective assessment metric is then based on both global geometric structures and local pixel correspondence. To evaluate color images, CIE L*a*b* color space is utilized. Experimental results are obtained to measure the performance of objective assessments with the proposed metric. The results show good consistency between the proposed objective metric and subjective assessment by human observers.
Year
DOI
Venue
2011
10.1111/j.1467-8659.2011.01881.x
COMPUTER GRAPHICS FORUM
Field
DocType
Volume
Computer vision,Image warping,Automatic image annotation,Feature detection (computer vision),Computer science,Image texture,Image processing,Image quality,Retargeting,Artificial intelligence,Digital image processing
Journal
30.0
Issue
ISSN
Citations 
2.0
0167-7055
37
PageRank 
References 
Authors
1.29
13
5
Name
Order
Citations
PageRank
Yong-Jin Liu183772.83
Xi Luo2371.29
Yuming Xuan3563.69
Wenfeng Chen4564.75
Xiaolan Fu578660.72