A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD-Reference-Cited by-同舟云学术

A WAVELET-BASED IMAGE QUALITY ASSESSMENT METHOD

Published:2008-07 Issue:04 Volume:06 Page:541-551
ISSN:0219-6913
Container-title:International Journal of Wavelets, Multiresolution and Information Processing
language:en
Short-container-title:Int. J. Wavelets Multiresolut Inf. Process.

Author:

LU WEN¹²,GAO XINBO¹²,TAO DACHENG³,LI XUELONG⁴

Affiliation:

1. School of Electronic Engineering, Xidian University, Xi'an 710071, P. R. China

2. State Key Lab of Integrated Service Networks, Xidian University, Xi'an 710071, P. R. China

3. Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

4. School of Computer Science and Information Systems, Birkbeck College, University of London, London WC1E 7HX, UK

Abstract

Image quality is a key characteristic in image processing,10,11 image retrieval,12,13 and biometrics.14 In this paper, a novel reduced-reference image quality assessment method is proposed based on wavelet transform. By simulating the human visual system, we take the variance of the visual sensitive coefficients into account to measure a distorted image. The computational complexity of the proposed method is much lower compared with some existing methods. Experimental results demonstrate its advantages in terms of correlation coefficient, outlier ratio, transmitted information, and CPU cost. Moreover, it is also illustrated that the proposed method has a good accordance with human subjective perception.

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Information Systems,Signal Processing

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219691308002501

Reference10 articles.

1. Image Quality Assessment: From Error Visibility to Structural Similarity

2. Z. Wang, H. R. Sheikh and A. C. Bovik, The Handbook of Video Databases: Design and Applications, eds. B. Furht and O. Marqure (CRC Press, 2003) pp. 1041–1078.

3. A theory for multiresolution signal decomposition: the wavelet representation

4. Wavelet-based color image compression: exploiting the contrast sensitivity function

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