  Image quality assessment (IQA) is an important element of a broad spectrum of
applications ranging from automatic video streaming to display technology.
Furthermore, the measurement of image quality requires a balanced investigation
of image content and features. Our proposed approach extracts visual features
by attaching global average pooling (GAP) layers to multiple Inception modules
of on an ImageNet database pretrained convolutional neural network (CNN). In
contrast to previous methods, we do not take patches from the input image.
Instead, the input image is treated as a whole and is run through a pretrained
CNN body to extract resolution-independent, multi-level deep features. As a
consequence, our method can be easily generalized to any input image size and
pretrained CNNs. Thus, we present a detailed parameter study with respect to
the CNN base architectures and the effectiveness of different deep features. We
demonstrate that our best proposal - called MultiGAP-NRIQA - is able to provide
state-of-the-art results on three benchmark IQA databases. Furthermore, these
results were also confirmed in a cross database test using the LIVE In the Wild
Image Quality Challenge database.
