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Image quality assessment measure based on natural image statistics in\n the Tetrolet domain

2014/12/08 by Abdelkaher Ait Abdelouahad, Abdelouahad, Abdelkaher Ait, Mohammed El Hassouni +5
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Image and Video Quality Assessment

paper · pdf · doi:10.48550/arxiv.1412.2697

openalex publication_date 2014/12/08 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28

Abstract

This paper deals with a reduced reference (RR) image quality measure based on\nnatural image statistics modeling. For this purpose, Tetrolet transform is used\nsince it provides a convenient way to capture local geometric structures. This\ntransform is applied to both reference and distorted images. Then, Gaussian\nScale Mixture (GSM) is proposed to model subbands in order to take account\nstatistical dependencies between tetrolet coefficients. In order to quantify\nthe visual degradation, a measure based on Kullback Leibler Divergence (KLD) is\nprovided. The proposed measure was tested on the Cornell VCL A-57 dataset and\ncompared with other measures according to FR-TV1 VQEG framework.\n

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