Image quality assessment measure based on natural image statistics in the Tetrolet domain
This work addresses image quality assessment for applications like video processing, but it appears incremental as it builds on existing statistical modeling techniques.
The paper tackled the problem of reduced reference image quality assessment by modeling natural image statistics using Tetrolet transform and Gaussian Scale Mixture, achieving results tested on the Cornell VCL A-57 dataset and compared with other measures under the FR-TV1 VQEG framework.
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.