  Image Quality Assessment algorithms predict a quality score for a pristine or
distorted input image, such that it correlates with human opinion. Traditional
methods required a non-distorted "reference" version of the input image to
compare with, in order to predict this score. However, recent "No-reference"
methods circumvent this requirement by modelling the distribution of clean
image features, thereby making them more suitable for practical use. However,
majority of such methods either use hand-crafted features or require training
on human opinion scores (supervised learning), which are difficult to obtain
and standardise. We explore the possibility of using deep features instead,
particularly, the encoded (bottleneck) feature maps of a Convolutional
Autoencoder neural network architecture. Also, we do not train the network on
subjective scores (unsupervised learning). The primary requirements for an IQA
method are monotonic increase in predicted scores with increasing degree of
input image distortion, and consistent ranking of images with the same
distortion type and content, but different distortion levels. Quantitative
experiments using the Pearson, Kendall and Spearman correlation scores on a
diverse set of images show that our proposed method meets the above
requirements better than the state-of-art method (which uses hand-crafted
features) for three types of distortions: blurring, noise and compression
artefacts. This demonstrates the potential for future research in this
relatively unexplored sub-area within IQA.
