  With the development of rendering techniques, computer graphics generated
images (CGIs) have been widely used in practical application scenarios such as
architecture design, video games, simulators, movies, etc. Different from
natural scene images (NSIs), the distortions of CGIs are usually caused by poor
rending settings and limited computation resources. What's more, some CGIs may
also suffer from compression distortions in transmission systems like cloud
gaming and stream media. However, limited work has been put forward to tackle
the problem of computer graphics generated images' quality assessment (CG-IQA).
Therefore, in this paper, we establish a large-scale subjective CG-IQA database
to deal with the challenge of CG-IQA tasks. We collect 25,454 in-the-wild CGIs
through previous databases and personal collection. After data cleaning, we
carefully select 1,200 CGIs to conduct the subjective experiment. Several
popular no-reference image quality assessment (NR-IQA) methods are tested on
our database. The experimental results show that the handcrafted-based methods
achieve low correlation with subjective judgment and deep learning based
methods obtain relatively better performance, which demonstrates that the
current NR-IQA models are not suitable for CG-IQA tasks and more effective
models are urgently needed.
