  Image quality assessment (IQA) continues to garner great interest in the
research community, particularly given the tremendous rise in consumer video
capture and streaming. Despite significant research effort in IQA in the past
few decades, the area of no-reference image quality assessment remains a great
challenge and is largely unsolved. In this paper, we propose a novel
no-reference image quality assessment system called Deep Quality, which
leverages the power of deep learning to model the complex relationship between
visual content and the perceived quality. Deep Quality consists of a novel
multi-scale deep convolutional neural network, trained to learn to assess image
quality based on training samples consisting of different distortions and
degradations such as blur, Gaussian noise, and compression artifacts.
Preliminary results using the CSIQ benchmark image quality dataset showed that
Deep Quality was able to achieve strong quality prediction performance (89%
patch-level and 98% image-level prediction accuracy), being able to achieve
similar performance as full-reference IQA methods.
