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A Color Intensity Invariant Low Level Feature Optimization Framework for Image Quality Assessment

N.K. Kottayil Irene Cheng Frederic Dufaux 1, 2 Anup Basu 1, 2 
1 MM - Multimédia
LTCI - Laboratoire Traitement et Communication de l'Information
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.
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Submitted on : Friday, January 13, 2017 - 12:04:49 AM
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  • HAL Id : hal-01433744, version 1


N.K. Kottayil, Irene Cheng, Frederic Dufaux, Anup Basu. A Color Intensity Invariant Low Level Feature Optimization Framework for Image Quality Assessment. Signal, Image and Video Processing, Springer Verlag, 2016, 10 (6), pp.1169-1176. ⟨hal-01433744⟩



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