Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images
This addresses the need for better evaluation metrics in AI image generation, though it is incremental as it builds on existing methods like transformers and MMD.
The paper tackles the problem of evaluating photorealistic quality in AI-generated images by introducing the Global-Local Image Perceptual Score (GLIPS), which outperforms existing metrics like FID and SSIM with higher correlation to human scores in tests across various generative models.
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.