Application Guided Image Quality Estimation Based on Classification

Image Quality (IQ) plays significant role for both human vision and artificial vision applications. This last decade, the number of camera increases exponentially, but the exploitation of the information depends on the quality of these acquired images, and sequences. A large number of objective blind IQ assessment (OBIQA) methods were proposed which find a global IQ estimation regardless the application. In this work, we assume that is impossible that such a method can be applied to all applications, however, for a given problem an objective model of the quality can be provided. Consequently, this paper addresses this issue for an automatic vehicle type classification application and proposes a novel OBIQA based classification approach. The proposed method first extracts a set of selective image features, then learns to classify images accordingly. In other terms, it aims to prevent misclassification and localize the source of poor images. Experiments show that it performs better than the state-of-the-art methods and can be used for similar applications.