  The process of rendering high dynamic range (HDR) images to be viewed on
conventional displays is called tone mapping. However, tone mapping introduces
distortions in the final image which may lead to visual displeasure. To
quantify these distortions, we introduce a novel no-reference quality
assessment technique for these tone mapped images. This technique is composed
of two stages. In the first stage, we employ a convolutional neural network
(CNN) to generate quality aware maps (also known as distortion maps) from tone
mapped images by training it with the ground truth distortion maps. In the
second stage, we model the normalized image and distortion maps using an
Asymmetric Generalized Gaussian Distribution (AGGD). The parameters of the AGGD
model are then used to estimate the quality score using support vector
regression (SVR). We show that the proposed technique delivers competitive
performance relative to the state-of-the-art techniques. The novelty of this
work is its ability to visualize various distortions as quality maps
(distortion maps), especially in the no-reference setting, and to use these
maps as features to estimate the quality score of tone mapped images.
