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A Color Intensity Invariant Low Level Feature Optimization Framework for Image Quality Assessment
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.
https://hal-imt.archives-ouvertes.fr/hal-01433744
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Submitted on : Friday, January 13, 2017 - 12:04:49 AM
Last modification on : Wednesday, November 3, 2021 - 6:18:49 AM
Contributor : Admin Télécom Paristech Connect in order to contact the contributor
Submitted on : Friday, January 13, 2017 - 12:04:49 AM
Last modification on : Wednesday, November 3, 2021 - 6:18:49 AM