Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13315
Title: Perceptual fidelity aware mean squared error
Authors: Xue, W
Mou, X
Zhang, L 
Feng, X
Keywords: Image restoration
Mean square error methods
Smoothing methods
Issue Date: 2013
Publisher: IEEE
Source: 2013 IEEE International Conference on Computer Vision (ICCV), 1-8 December 2013, Sydney, NSW, p. 705-712 How to cite?
Abstract: How to measure the perceptual quality of natural images is an important problem in low level vision. It is known that the Mean Squared Error (MSE) is not an effective index to describe the perceptual fidelity of images. Numerous perceptual fidelity indices have been developed, while the representatives include the Structural SIMilarity (SSIM) index and its variants. However, most of those perceptual measures are nonlinear, and they cannot be easily dopted as an objective function to minimize in various low level vision tasks. Can MSE be perceptual fidelity aware after some minor adaptation? In this paper we propose a simple framework to enhance the perceptual fidelity awareness of MSE by introducing an l2-norm structural error term to it. Such a Structural MSE (SMSE) can lead to very competitive image quality assessment (IQA) results. More surprisingly, we show that by using certain structure extractors, SMSE can be further turned into a Gaussian smoothed MSE (i.e., the Euclidean distance between the original and distorted images after Gaussian smooth filtering), which is much simpler to calculate but achieves rather better IQA performance than SSIM. The so called Perceptual-fidelity Aware MSE (PAMSE) can have great potentials in applications such as perceptual image coding and perceptual image restoration.
URI: http://hdl.handle.net/10397/13315
ISBN: 
ISSN: 1550-5499
DOI: 10.1109/ICCV.2013.93
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

6
Last Week
3
Last month
0
Citations as of Oct 17, 2017

WEB OF SCIENCETM
Citations

4
Last Week
0
Last month
0
Citations as of Oct 2, 2017

Page view(s)

77
Last Week
0
Last month
Checked on Oct 16, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.