Objective
Quality Assessment for Image Retargeting
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Image
retargeting techniques aim to obtain retargeted images with different sizes or
aspect ratios for various display screens. Various content-aware image
retargeting algorithms have been proposed recently. However, there is still no
effective objective metric for visual quality assessment of retargeted images.
In this paper, we propose a novel full-reference objective metric for assessing
visual quality of a retargeted image based on perceptual geometric distortion
and information loss. The proposed metric measures the geometric distortion of retargeted images based
on the local variance of SIFT flow vector fields. Furthermore, a visual
saliency map is derived to characterize human perception of the geometric
distortion. Besides, the information loss in a
retargeted image, which is estimated based on the saliency map, is
also taken into account in the proposed metric. Subjective tests are conducted
to evaluate the performance of the proposed metric. Experimental results show
the good consistency between the proposed objective metric and the subjective
rankings.
Menu
Code
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EMD
·
Scaling
·
Warping
Which
one is better?
SIFT
Flow estimation: dense
correspondence estimation using method in [1].
Saliency
detection: saleicny map estimation using method in [2].
PGD measurement: flow field analysis for geometric
distortion with human visual system.
SLR measurement:
information loss estimation by calculating the ratio between the saliency map
of the original image and that of retargeted one.
Rank correlation comparison for PGD,
SLR, and both.
Rank
correlation comparison for the proposed metric and others.
Visualized
Results
Fig.4 (a) The original image, and the
visualized perceptual distortion maps obtained from (b) multi-operator method,
(c) seam carving, (d) Shift-map, and (e) warping method. Their overall quality
indices are qtotal = 0.88, 0.42, 0.8, and 0.65, respectively.
Reference
[1] C. Liu, J. Yuen, and A. Torralba,
“Dense correspondence across scenes and its applications,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 978-994, May 2011.
[2] Y. Fang, W. Lin, Z. Chen, and C.-W. Lin, “Saliency detection in the
compressed domain for adaptive image retargeting,” IEEE Trans. Image Process.
vol. 21, no. 9, pp. 3888-3901, Sept. 2012.