R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment

Zhuangzi Li; Jian Jin; Shilv Cai; Weisi Lin

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arXiv · 2603.10578

R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment

Zhuangzi Li·Jian Jin·Shilv Cai·Weisi Lin

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Abstract

Immersive Computer Graphics (CGs) rendering has become ubiquitous in modern daily life. However, comprehensively evaluating CG quality remains challenging for two reasons: First, existing CG datasets lack systematic descriptions of rendering quality; and second existing CG quality assessment methods cannot provide reasonable text-based explanations. To address these issues, we first identify six key perceptual dimensions of CG quality from the user perspective and construct a dataset of 3500 CG images with corresponding quality descriptions. Each description covers CG style, content, and perceived quality along the selected dimensions. Furthermore, we use a subset of the dataset to build several question-answer benchmarks based on the descriptions in order to evaluate the responses of existing Vision Language Models (VLMs). We find that current VLMs are not sufficiently accurate in judging fine-grained CG quality, but that descriptions of visually similar images can significantly improve a VLM's understanding of a given CG image. Motivated by this observation, we adopt retrieval-augmented generation and propose a two-stream retrieval framework that effectively enhances the CG quality assessment capabilities of VLMs. Experiments on several representative VLMs demonstrate that our method substantially improves their performance on CG quality assessment.

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BibTeX RIS

Zhuangzi Li, Jian Jin, Shilv Cai, Weisi Lin. 2026-03-11. R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment. https://arxiv.org/abs/2603.10578

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