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.

Document type :
Journal articles
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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, February 20, 2019 - 2:38:43 PM

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  • HAL Id : hal-01433744, version 1

Citation

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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