Open AccessQuantifying blur in colour images using higher order singular values
Author(s) -
Qureshi M.A.,
Deriche M.,
Beghdadi A.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.1792
Subject(s) - artificial intelligence , computer science , metric (unit) , computer vision , luminance , singular value decomposition , image quality , rgb color model , channel (broadcasting) , benchmarking , singular value , image (mathematics) , pattern recognition (psychology) , engineering , computer network , operations management , eigenvalues and eigenvectors , physics , marketing , quantum mechanics , business
This Letter introduces a novel framework for blind blur assessment in colour images using higher order singular values. The RGB colour image is seen as a third‐order tensor to exploit the spatial and inter‐channel correlations, so that blurring effects are captured more robustly. The tensor is decomposed into different two‐dimensional matrices, also called unfoldings. The conventional singular value decomposition is carried out for these unfoldings instead of computing it for the luminance component alone. The experiments were performed on several publicly available databases and the results validate the superiority of the proposed metric among different state‐of‐the‐art blind blur assessment metrics. The proposed framework for image quality assessment (IQA) from colour images fits well with the current trends and research efforts put in enhancing the quality of experience for different multimedia applications and in benchmarking new imaging and sensing technologies including camera and other vision systems with IQA capabilities.