A color intensity invariant low-level feature optimization framework for image quality assessment. Issue 6 (September 2016)

• Record Type: Journal Article
• Authors: Kottayil, Navaneeth; Cheng, Irene; Dufaux, Frederic; Basu, Anup
• Is Part Of: Signal, image and video processing. Volume 10:Issue 6(2016)
• Page Start: 1169
• Page End: 1176
• Publication Date: 2016-09
• Subjects: Low-level feature extraction -- Perceptual quality -- Color adaptation -- Image quality assessment (IQA) -- Center-surround frequency scaling -- Two-tier normalization; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0873-x
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Image quality assessment (IQA) algorithms evaluate the perceptual quality of an image using evaluation scores that assess the similarity or difference between two images. We propose a new low-level feature-based IQA technique, which applies filter-bank decomposition and center-surround methodology. Differing from existing methods, our model incorporates color intensity adaptation and frequency scaling optimization at each filter-bank level and spatial orientation to extract and enhance perceptually significant features. Our computational model exploits the concept of object detection and encapsulates characteristics proposed in other IQA algorithms in a unified architecture. We also propose a systematic approach to review the evolution of IQA algorithms using unbiased test datasets, instead of looking at individual scores in isolation. Experimental results demonstrate the feasibility of our approach.