This article proposes a new no-reference image quality assessment method that is able to blindly predict the quality of an image. The method is based on a machine learning technique that uses texture descriptors. In the proposed method, texture features are computed by decomposing images into texture information using multiscale local binary pattern (MLBP) operators. In particular, the parameters of local binary pattern operators are varied, which generates MLBP operators. The features used for training the prediction algorithm are the histograms of these MLBP channels. The results show that, when compared with other state-of-the-art no-reference methods, the proposed method is competitive in terms of prediction precision and computational complexity. © 2016 Society for Imaging Science and Technology.
Pedro Garcia Freitas, Welington Y. L. Akamine, Mylène C. Q. Farias, "Blind Image Quality Assessment Using Multiscale Local Binary Patterns" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XIV, 2017, pp 7 - 14, https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-218