6533b85cfe1ef96bd12bc7ee

RESEARCH PRODUCT

Image Quality Assessment Measure Based on Natural Image Statistics in the Tetrolet Domain

Driss AboutajdineMohammed El HassouniHocine CherifiAbdelkaher Ait Abdelouahad

subject

Kullback–Leibler divergenceImage qualitybusiness.industryTetrolet transformPattern recognitionMeasure (mathematics)Domain (software engineering)Image (mathematics)GSMGaussian scale mixturesStatisticsArtificial intelligencebusinessMathematics

description

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.

yearjournalcountryeditionlanguage
2012-01-01
https://doi.org/10.1007/978-3-642-31254-0_51