By Topic

New image quality metric using derivative filters and compressive sensing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
D. -O Kim ; Department of Electronic Engineering, School of Engineering, Sogang University, Sinsu-dong 1, Mapo-gu, Seoul 121-742, Korea ; R. -H. Park ; J. W. Lee

In this paper, we propose a new image quality metric using derivative filters in the context of compressive sensing (CS) that represents a sparse or compressible signal with a small number of measurements. In general, an arbitrary image is not sparse or compressible, however, its derivative image is compressible. In this paper, derivative images are obtained using first- and second-order derivative filters such as Sobel operators and Laplacian of Gaussian filters. Each derivative image of the reference and distorted images is measured via CS. Measurements of derivative images are compared for evaluating the image quality. Experiments with the laboratory for image and video engineering database show the effectiveness of the proposed method.

Published in:

2010 IEEE International Conference on Image Processing

Date of Conference:

26-29 Sept. 2010