  Automatically learned quality assessment for images has recently become a hot
topic due to its usefulness in a wide variety of applications such as
evaluating image capture pipelines, storage techniques and sharing media.
Despite the subjective nature of this problem, most existing methods only
predict the mean opinion score provided by datasets such as AVA [1] and TID2013
[2]. Our approach differs from others in that we predict the distribution of
human opinion scores using a convolutional neural network. Our architecture
also has the advantage of being significantly simpler than other methods with
comparable performance. Our proposed approach relies on the success (and
retraining) of proven, state-of-the-art deep object recognition networks. Our
resulting network can be used to not only score images reliably and with high
correlation to human perception, but also to assist with adaptation and
optimization of photo editing/enhancement algorithms in a photographic
pipeline. All this is done without need for a "golden" reference image,
consequently allowing for single-image, semantic- and perceptually-aware,
no-reference quality assessment.
