Reduced reference image quality assessment based on statistics in empirical mode decomposition domain
Affiliation auteurs | !!!! Error affiliation !!!! |
Titre | Reduced reference image quality assessment based on statistics in empirical mode decomposition domain |
Type de publication | Journal Article |
Year of Publication | 2014 |
Auteurs | Abdelouahad AAit, Hassouni MEl, Cherifi H, Aboutajdine D |
Journal | SIGNAL IMAGE AND VIDEO PROCESSING |
Volume | 8 |
Pagination | 1663-1680 |
Date Published | NOV |
Type of Article | Article |
ISSN | 1863-1703 |
Mots-clés | Generalized Gaussian density, Intrinsic mode functions, Kullback-Leibler divergence, Reduced reference image quality assessment, support vector machine |
Résumé | This paper deals with the image quality assessment (IQA) task using a natural image statistics approach. A reduced reference (RRIQA) measure based on the bidimensional empirical mode decomposition is introduced. First, we decompose both, reference and distorted images, into intrinsic mode functions (IMF) and then we use the generalized Gaussian density (GGD) to model IMF coefficients of the reference image. Finally, we measure the impairment of a distorted image by fitting error between the IMF coefficients histogram of the distorted image and the estimated IMF coefficients distribution of the reference image, using the Kullback-Leibler divergence (KLD). Furthermore, to predict the quality, we propose a new support vector machine-based (SVM) classification approach as an alternative to logistic function-based regression. In order to validate the proposed measure, three benchmark datasets are involved in our experiments. Results demonstrate that the proposed metric compare favorably with alternative solutions for a wide range of degradation encountered in practical situations. |
DOI | 10.1007/s11760-012-0407-0 |