6533b839fe1ef96bd12a6337

RESEARCH PRODUCT

Tetrolet-based reduced reference image quality assessment approach

Abdelkaher Ait AbdelouahadMounir OmariHocine CherifiBrahim AlibouchMohammed El Hassouni

subject

Reference imageKullback–Leibler divergencebusiness.industryComputer scienceDistortionPattern recognitionProbability density functionArtificial intelligencebusinessMeasure (mathematics)Domain (mathematical analysis)Haar waveletImage (mathematics)

description

In this paper, we propose a new reduced reference image quality assessment (RRIQA) scheme. For this purpose, we use a statistical-based method in a new adaptive Haar wavelet transform domain, called Tetrolet. Firstly, we decompose the reference and distorted images and we obtain the Tetrolet coefficients for each image. Secondly, we use a marginal Generalized Gaussian Density (GGD) to model each subband coefficients. Finally, the distortion measure is computed using the Kullback Leibler Divergence (KLD) between GGD Probability density function (PDFs). Experimental results show the efficiency of the proposed method when comparing to those reported in the literature.

yearjournalcountryeditionlanguage
2014-04-012014 International Conference on Multimedia Computing and Systems (ICMCS)
https://doi.org/10.1109/icmcs.2014.6911178