Quality Assessment of Screen Content Images in Wavelet Domain

Objective image quality assessment (IQA) aims to predict human judgment on an image's visual quality. This computational model is applicable in various scenarios, such as monitoring, image compression and image restoration. However, the research in this area began with natural scenes while image contents has become much more diverse. Remote computing and screen sharing applications led to the prevalence of screen content images (SCIs), which are full of texts, flat areas, and also natural scenes. Due to their different characteristics, traditional methods for IQA are insufficient for SCIs. In this paper, we show that capturing the distortions in the horizontal and vertical structures of an SCI is effective for predicting its quality. We also take into account the human visual system's trend to analyze a scene in a multi-scale fashion. Based on these observations, we propose wavelet analysis as a means to accomplish the mentioned strategies. Experimental results on three SCI quality datasets show that plausible predictions can be made by the proposed full-reference method, while having an acceptable time complexity.