Natural DCT statistics approach to no-reference image quality assessmentReport as inadecuate

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1 LIVE 2 Equipe Image - Laboratoire GREYC - UMR6072 GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen

Abstract : General-purpose no-reference image quality assessment approaches still lag the advances in full-reference methods. Most no-reference methods are either distortion specific i.e. they quantify one or more distortions such as blur, blockiness, or ringing, or they train a learning machine based on a large number of features. In this approach, we propose a discrete cosine transform DCT statistics-based support vector machine SVM approach based on only 3 features in the DCT domain. The approach extracts a very small number of features and is entirely in the DCT domain, making it computationally convenient. The results are shown to correlate highly with human visual perception of quality.

Keywords : support vector machine No-reference image quality assessment discrete cosine transform anisotropy entropy support vector machine.

Author: Michele Saad - Alan C. Bovik - Christophe Charrier -



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