  In this paper, we train independent linear decoder models to estimate the
perceived quality of images. More specifically, we calculate the responses of
individual non-overlapping image patches to each of the decoders and scale
these responses based on the sharpness characteristics of filter set. We use
multiple linear decoders to capture different abstraction levels of the image
patches. Training each model is carried out on 100,000 image patches from the
ImageNet database in an unsupervised fashion. Color space selection and ZCA
Whitening are performed over these patches to enhance the descriptiveness of
the data. The proposed quality estimator is tested on the LIVE and the TID 2013
image quality assessment databases. Performance of the proposed method is
compared 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 in
all categories.
