Open AccessImage quality assessment scheme with topographic independent components analysis for sparse feature extraction
Author(s) -
Ding Yong,
Dai Hang,
Wang Shaoze
Publication year - 2014
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.2013.4298
Subject(s) - metric (unit) , distortion (music) , feature (linguistics) , feature extraction , artificial intelligence , computer science , image quality , pattern recognition (psychology) , quality (philosophy) , image (mathematics) , data mining , scheme (mathematics) , computer vision , mathematics , engineering , linguistics , philosophy , mathematical analysis , epistemology , amplifier , computer network , operations management , bandwidth (computing)
A no‐reference objective metric for image quality assessment by integrating the topographic independent components analysis into feature extraction is presented. By taking the topographic relationship among the initially independent features into consideration, it extracts the features of more sparsity or independency which is essentially related to inherent quality. Evaluation results demonstrate that the proposed metric is able to predict the image quality accurately across various distortion types.