Multivariate Statistical Approach to Image Quality Tasks

Journal of Imaging

Praful Gupta¹  Alan C. Bovik¹  Jack L. Glover²  Nicholas G. Paulter²  Christos G. Bampis³ 

[1] Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA;National Institute of Standards and Technology, Gaithersburg, MD 20899, USA;Netflix Inc., Los Gatos, CA 95032, USA;

关键词: image quality assessment;    generalized contrast normalization;    multivariate statistical modeling;    X-ray images;   

DOI  :  10.3390/jimaging4100117

来源: DOAJ

【 摘 要 】

Many existing natural scene statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here, we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-sensitive image quality information. We also demonstrate the violation of Gaussianity assumptions that occur when locally estimating the energies of distorted image coefficients. Thus, we propose a generalized Gaussian-based local contrast estimator as a way to implement non-linear local gain control, which facilitates the accurate modeling of both pristine and distorted images. We integrate the novel approach of generalized contrast normalization with multivariate modeling of bandpass image coefficients into a holistic NR IQA model, which we refer to as multivariate generalized contrast normalization (MVGCN). We demonstrate the improved performance of MVGCN on quality-relevant tasks on multiple imaging modalities, including visible light image quality prediction and task success prediction on distorted X-ray images.

【 授权许可】

Unknown