Computer graphics images (CGIs) are artificially generated by means of
computer programs and are widely perceived under various scenarios, such as
games, streaming media, etc. In practical, the quality of CGIs consistently
suffers from poor rendering during the production and inevitable compression
artifacts during the transmission of multimedia applications. However, few
works have been dedicated to dealing with the challenge of computer graphics
images quality assessment (CGIQA). Most image quality assessment (IQA) metrics
are developed for natural scene images (NSIs) and validated on the databases
consisting of NSIs with synthetic distortions, which are not suitable for
in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and
CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000
CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled
laboratory environment to obtain the accurate perceptual ratings of the CGIs.
Then, we propose an effective deep learning-based no-reference (NR) IQA model
by utilizing multi-stage feature fusion strategy and multi-stage channel
attention mechanism. The major motivation of the proposed model is to make full
use of inter-channel information from low-level to high-level since CGIs have
apparent patterns as well as rich interactive semantic content. Experimental
results show that the proposed method outperforms all other state-of-the-art NR
IQA methods on the constructed CGIQA-6k database and other CGIQA-related
databases. The database along with the code will be released to facilitate
further research