Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks

In this work we describe a compact multi-task Convolutional Neural Network (CNN) for simultaneously estimating image quality and identifying distortions. CNNs are natural choices for multi-task problems because learned convolutional features may be shared by different high level tasks. However, we empirically argue that simply appending additional tasks based on the state of the art structure (e.g., 1) does not lead to optimal solutions. We design a compact structure with nearly 90% fewer parameters compared to 1, and demonstrate its learning power.