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 will be released to facilitate further research.

翻译：计算机图形图像（CGIs）是通过计算机程序人工生成的，在游戏、流媒体等多种场景中被广泛感知。实践中，CGIs 的质量持续受到生产过程中的不良渲染、多媒体应用传输中不可避免的压缩伪影以及构图和设计不良导致的低审美质量的影响。然而，专门应对计算机图形图像质量评价（CGIQA）挑战的工作较少。大多数图像质量评价（IQA）指标是为自然场景图像（NSIs）开发的，并在由带有合成失真的NSIs组成的数据库上进行验证，这些指标不适用于野外CGIs。为弥合NSIs与CGIs质量评价之间的差距，我们构建了一个包含6,000张CGIs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，以获得CGIs的精确感知评分。随后，我们提出了一种有效的基于深度学习的无参考（NR）IQA模型，该模型同时利用了失真和审美质量表示。实验结果表明，所提出的方法在所构建的CGIQA-6k数据库及其他与CGIQA相关的数据库上，均优于所有其他最先进的NR IQA方法。该数据库将予以公开，以促进进一步研究。