Color image quality assessment measure using multivariate generalized Gaussian distribution
This is an incremental improvement for color image processing, offering a reduced-reference method with minimal original image information.
The paper tackled color image quality assessment by modeling steerable pyramid coefficients with a Multivariate Generalized Gaussian Distribution to account for RGB correlations, and results on the TID 2008 benchmark showed effectiveness across various distortion types.
This paper deals with color image quality assessment in the reduced-reference framework based on natural scenes statistics. In this context, we propose to model the statistics of the steerable pyramid coefficients by a Multivariate Generalized Gaussian distribution (MGGD). This model allows taking into account the high correlation between the components of the RGB color space. For each selected scale and orientation, we extract a parameter matrix from the three color components subbands. In order to quantify the visual degradation, we use a closed-form of Kullback-Leibler Divergence (KLD) between two MGGDs. Using "TID 2008" benchmark, the proposed measure has been compared with the most influential methods according to the FRTV1 VQEG framework. Results demonstrates its effectiveness for a great variety of distortion type. Among other benefits this measure uses only very little information about the original image.