This paper deals with the typical set of an image quality assessment (IQA) measure. In particular, it focuses on the well known and widely used Structural SIMilarity index (SSIM). In agreement with Information Theory, the visual distortion typical set is composed of the least amount of information necessary to estimate the quality of the distorted image. General criteria for an effective and fruitful computation of the set will be given. As it will be shown, the typical set allows to increase IQA efficiency by considerably speeding up its computation, thanks to the reduced number of image blocks used for the evaluation of the considered IQA metric. Copyright © 2014 SCITEPRESS.
A fast computation method for IQA metrics based on their typical set
Vitulano D
2014
Abstract
This paper deals with the typical set of an image quality assessment (IQA) measure. In particular, it focuses on the well known and widely used Structural SIMilarity index (SSIM). In agreement with Information Theory, the visual distortion typical set is composed of the least amount of information necessary to estimate the quality of the distorted image. General criteria for an effective and fruitful computation of the set will be given. As it will be shown, the typical set allows to increase IQA efficiency by considerably speeding up its computation, thanks to the reduced number of image blocks used for the evaluation of the considered IQA metric. Copyright © 2014 SCITEPRESS.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
