No-reference multiscale blur detection tool for content based image retrieval

Soundararajan Ezekiel; Russell Stocker; Kyle Harrity; Mark Alford; David Ferris; Erik Blasch; Mark Gorniak

doi:10.1117/12.2058062

19 June 2014 No-reference multiscale blur detection tool for content based image retrieval

Soundararajan Ezekiel, Russell Stocker, Kyle Harrity, Mark Alford, David Ferris, Erik Blasch, Mark Gorniak

Proceedings Volume 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II; 90890I (2014) https://doi.org/10.1117/12.2058062
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

In recent years, digital cameras have been widely used for image capturing. These devices are equipped in cell phones, laptops, tablets, webcams, etc. Image quality is an important component of digital image analysis. To assess image quality for these mobile products, a standard image is required as a reference image. In this case, Root Mean Square Error and Peak Signal to Noise Ratio can be used to measure the quality of the images. However, these methods are not possible if there is no reference image. In our approach, a discrete-wavelet transformation is applied to the blurred image, which decomposes into the approximate image and three detail sub-images, namely horizontal, vertical, and diagonal images. We then focus on noise-measuring the detail images and blur-measuring the approximate image to assess the image quality. We then compute noise mean and noise ratio from the detail images, and blur mean and blur ratio from the approximate image. The Multi-scale Blur Detection (MBD) metric provides both an assessment of the noise and blur content. These values are weighted based on a linear regression against full-reference y values. From these statistics, we can compare to normal useful image statistics for image quality without needing a reference image. We then test the validity of our obtained weights by R² analysis as well as using them to estimate image quality of an image with a known quality measure. The result shows that our method provides acceptable results for images containing low to mid noise levels and blur content.

Citation Download Citation

Soundararajan Ezekiel, Russell Stocker, Kyle Harrity, Mark Alford, David Ferris, Erik Blasch, and Mark Gorniak "No-reference multiscale blur detection tool for content based image retrieval", Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 90890I (19 June 2014); https://doi.org/10.1117/12.2058062

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available