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 practice, the quality of CGIs consistently suffers from poor rendering during production, inevitable compression artifacts during the transmission of multimedia applications, and low aesthetic quality resulting from poor composition and design. However, few works have been dedicated to dealing with the challenge of computer graphics image quality assessment (CGIQA). Most image quality assessment (IQA) metrics are developed for natural scene images (NSIs) and validated on 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 both distortion and aesthetic quality representation. 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 is released at https://github.com/zzc-1998/CGIQA6K.

翻译：计算机图形图像是通过计算机程序人工生成的，广泛应用于游戏、流媒体等多种场景。然而，在多媒体应用的制作过程中，其质量常受限于渲染不足、传输中的压缩伪影，以及因构图和设计不佳导致的较低审美质量。目前，针对计算机图形图像质量评估（CGIQA）的研究较少。大多数图像质量评估（IQA）指标是为自然场景图像（NSI）设计的，并基于包含合成失真的NSI数据库进行验证，这些方法不适用于野外CGI。为弥补NSI与CGI质量评估之间的差距，我们构建了一个包含6000张CGI的大规模野外CGIQA数据库（CGIQA-6k），并在严格受控的实验室环境中进行主观实验，以获取CGI的精确感知评分。随后，我们提出了一种基于深度学习的有效无参考（NR）IQA模型，该模型同时利用失真和审美质量表征。实验结果表明，所提方法在构建的CGIQA-6k数据库及其他CGIQA相关数据库上，均优于现有最先进的NR IQA方法。该数据库已发布于https://github.com/zzc-1998/CGIQA6K。