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In this paper, we train independent linear decoder models to estimate theperceived quality of images. More specifically, we calculate the responses ofindividual non-overlapping image patches to each of the decoders and scalethese responses based on the sharpness characteristics of filter set. We usemultiple linear decoders to capture different abstraction levels of the imagepatches. Training each model is carried out on 100,000 image patches from theImageNet database in an unsupervised fashion. Color space selection and ZCAWhitening are performed over these patches to enhance the descriptiveness ofthe data. The proposed quality estimator is tested on the LIVE and the TID 2013image quality assessment databases. Performance of the proposed method iscompared against eleven other state of the art methods in terms of accuracy,consistency, linearity, and monotonic behavior. Based on experimental results,the proposed method is generally among the top performing quality estimators inall categories.

MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation