Image quality assessment measure based on natural image statistics in the tetrolet domain

Authors:
Abdelkaher Ait Abdelouahad;Mohammed El Hassouni;Hocine Cherifi;Driss Aboutajdine
Affiliations:
LRIT URAC, University of Mohammed V-Agdal, Morocco;DESTEC, FLSHR, University of Mohammed V-Agdal, Morocco;Le2i-UMR CNRS 5158, University of Burgundy, Dijon, France;LRIT URAC, University of Mohammed V-Agdal, Morocco
Venue:
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Year:
2012

Citing 4
Cited 0

Reduced-reference IQA in contourlet domain

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Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation

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Image quality assessment: from error visibility to structural similarity

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Abstract

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