Learning Deep Blind Quality Assessment for Cartoon Images.

Abstract

Although the cartoon industry has developed rapidly in recent years, few studies pay special attention to cartoon image quality assessment (IQA). Unfortunately, applying blind natural IQA algorithms directly to cartoons often leads to inconsistent results with subjective visual perception. Hence, this brief proposes a blind cartoon IQA method based on convolutional neural networks (CNNs). Note that training a robust CNN depends on manually labeled training sets. However, for a large number of cartoon images, it is very time-consuming and costly to manually generate enough mean opinion scores (MOSs). Therefore, this brief first proposes a full reference (FR) cartoon IQA metric based on cartoon-texture decomposition and then uses the estimated FR index to guide the no-reference IQA network. Moreover, in order to improve the robustness of the proposed network, a large-scale dataset is established in the training stage, and a stochastic degradation strategy is presented, which randomly implements different degradations with random parameters. Experimental results on both synthetic and real-world cartoon image datasets demonstrate the effectiveness and robustness of the proposed method.