Generalized Gaussian scale mixtures: A model for wavelet coefficients of natural images

Gupta, Praful and Moorthy, Anush Krishna and Soundararajan, Rajiv and Bovik, Alan Conrad (2018) Generalized Gaussian scale mixtures: A model for wavelet coefficients of natural images. In: SIGNAL PROCESSING-IMAGE COMMUNICATION, 66 . pp. 87-94.

PDF Sig_Pro_Ima_Com_66_87_2018.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

We develop a Generalized Gaussian scale mixture (GGSM) model of the wavelet coefficients of natural and distorted images. The GGSM model, which is more general than and which subsumes the Gaussian scale mixture (GSM) model, is shown to be a better representation of the statistics of the wavelet coefficients of both natural as well as distorted images. We demonstrate the utility of the model by applying it to various image processing applications, including blind distortion identification and no reference image quality assessment (NR-IQA). Similar to the GSM model, the GGSM model is useful for motivating the use of local divisive energy normalization, especially when the wavelet coefficients are computed on distorted pictures. We show that the GGSM model can lead to improved performance in distortion-related applications, while providing a more principled approach to the statistical processing of distorted image signals. The software release of a GGSM-based NR-IQA approach called DIIVINE-GGSM is available online at http://live.ece.utexas.edu/research/quality/diivine-ggsm.zip for further experimentation.

Item Type:	Journal Article
Publication:	SIGNAL PROCESSING-IMAGE COMMUNICATION
Publisher:	ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Additional Information:	Copyright of this article belong to ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	24 Jul 2018 14:55
Last Modified:	24 Jul 2018 14:55
URI:	http://eprints.iisc.ac.in/id/eprint/60276

Actions (login required)

View Item