This paper introduces the Global-Local Image Perceptual Score (GLIPS), an
image metric designed to assess the photorealistic image quality of
AI-generated images with a high degree of alignment to human visual perception.
Traditional metrics such as FID and KID scores do not align closely with human
evaluations. The proposed metric incorporates advanced transformer-based
attention mechanisms to assess local similarity and Maximum Mean Discrepancy
(MMD) to evaluate global distributional similarity. To evaluate the performance
of GLIPS, we conducted a human study on photorealistic image quality.
Comprehensive tests across various generative models demonstrate that GLIPS
consistently outperforms existing metrics like FID, SSIM, and MS-SSIM in terms
of correlation with human scores. Additionally, we introduce the Interpolative
Binning Scale (IBS), a refined scaling method that enhances the
interpretability of metric scores by aligning them more closely with human
evaluative standards. The proposed metric and scaling approach not only
provides more reliable assessments of AI-generated images but also suggest
pathways for future enhancements in image generation technologies.

该研究引入了全局 - 局部图像感知评分（GLIPS），一种用于评估人工智能生成图像与人类视觉感知高度一致的真实感图像质量的图像度量。GLIPS 利用先进的基于变换器的注意机制评估局部相似性和最大均值差异（MMD）评估全局分布相似性。人类研究表明，GLIPS 在真实感图像质量的相关性方面相比现有的指标如 FID、SSIM 和 MS-SSIM 具有更好的表现。此外，该研究还引入了插值缩放方法（IBS），通过使度量分数与人类评估标准更加一致，提高了度量分数的可解释性。该提出的度量和缩放方法不仅提供了更可靠的对人工智能生成图像的评估，还为图像生成技术的未来改进提供了指导。

全局和局部图像感知评分（GLIPS）：评估人工智能生成图像的真实感质量

Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic  Quality of AI-Generated Images

Novel view synthesis is a challenging and ill-posed inverse rendering
problem. Neural rendering techniques have recently achieved photorealistic
image quality for this task. State-of-the-art (SOTA) neural volume rendering
approaches, however, are slow to train and require minutes of inference (i.e.,
rendering) time for high image resolutions. We adopt high-capacity neural scene
representations with periodic activations for jointly optimizing an implicit
surface and a radiance field of a scene supervised exclusively with posed 2D
images. Our neural rendering pipeline accelerates SOTA neural volume rendering
by about two orders of magnitude and our implicit surface representation is
unique in allowing us to export a mesh with view-dependent texture information.
Thus, like other implicit surface representations, ours is compatible with
traditional graphics pipelines, enabling real-time rendering rates, while
achieving unprecedented image quality compared to other surface methods. We
assess the quality of our approach using existing datasets as well as
high-quality 3D face data captured with a custom multi-camera rig.

采用高容量神经场景表示和周期激活联合优化场景的隐式表面和辐射场的神经渲染管道，加速了现有技术水平的神经体积渲染，实现了与传统图形管道兼容的隐式表面表示，并实现了与其他表面方法相比具有前所未有的图像质量。