For decades, image quality analysis pipeline has been using filters that are derived from human vision system. Although this paradigm is able to capture the basic aspects of human vision, it falls short of characterizing the complex human perception of different visual appearance
and image quality. In this work, we propose a new framework that leverages the image recognition capabilities of convolution neural networks to distinguish the visual differences between uniform halftone target samples that are printed on different media using the same printing technology.
First, for each scanned target sample, a pre-trained Residual Neural Network is used to generate 2,048-dimension vision feature vector. Then, Principal Component Analysis is used to reduce the dimension to 48 components, which is then used to train a Support Vector Machine to classify the
target images. Our model has been tested on various classification and regression tasks and shows very good performance. Further analysis shows that our neural-network-based image quality model learns to makes decisions based on the frequencies of color variations within the target image,
and it is capable of characterizing the visual differences under different printer settings.