  The goal of full-reference image quality assessment (FR-IQA) is to predict
the quality of an image as perceived by human observers with using its
pristine, reference counterpart. In this study, we explore a novel, combined
approach which predicts the perceptual quality of a distorted image by
compiling a feature vector from convolutional activation maps. More
specifically, a reference-distorted image pair is run through a pretrained
convolutional neural network and the activation maps are compared with a
traditional image similarity metric. Subsequently, the resulted feature vector
is mapped onto perceptual quality scores with the help of a trained support
vector regressor. A detailed parameter study is also presented in which the
design choices of the proposed method is reasoned. Furthermore, we study the
relationship between the amount of training images and the prediction
performance. Specifically, it is demonstrated that the proposed method can be
trained with few amount of data to reach high prediction performance. Our best
proposal - ActMapFeat - is compared to the state-of-the-art on six publicly
available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID,
CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform
the state-of-the-art on these benchmark databases.
