出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper, a new no-reference image quality assessment (NR-IQA) metric for grey images is
proposed using LIVE II image database. The features used are extracted from three well-known
NR-IQA objective metrics based on natural scene statistical attributes from three different
domains. These metrics may contain redundant, noisy or less informative features which affect
the quality score prediction. In order to overcome this drawback, the first step of our work
consists in selecting the most relevant image quality features by using Singular Value
Decomposition (SVD) based dominant eigenvectors. The second step is performed by employing
Relevance Vector Machine (RVM) to learn the mapping between the previously selected
features and human opinion scores. Simulations demonstrate that the proposed metric performs
very well in terms of correlation and monotonicity.
关键词：Natural Scene Statistics (NSS); Singular Value Decomposition (SVD); dominant eigenvectors;
Relevance Vector Machine (RVM)
