## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.1. **Evaluation Metrics for Image Processing:**  
_Inception Score (IS)_: The inception score (IS) is a widely used metric for evaluating the quality of generated images in generative adversarial networks (GANs). It measures both the diversity and realness of the generated images. The IS is calculated by utilizing the inception model and computing the KL divergence between the conditional class distribution of the generated samples and the marginal class distribution. In addition to IS, several models mentioned earlier utilize this metric for assessing the quality of generated images. These models include text-conditioned auxiliary classifier generative adversarial network (TAC-GAN) [84], attentional generative adversarial networks (AttnGAN) [92], CogView [77], and stacked generative adversarial networks (StackGAN) [93]. _Structural Similarity Index (SSIM)_: The structural similarity index (SSIM) is a metric for image quality assessment that considers changes in structural information as the primary factor influencing visual quality. Unlike other methods that focus on the errors between the original and compressed image, SSIM considers changes in structural information as the main element affecting visual quality. This metric has found applications in various image and video processing models, including DiffusionCLIP [131], mDCSRN [137], alignDRAW [78], SRGAN [140], and VSRResFeatGAN [91]. These models utilize SSIM to  
Figure 18. Evaluation metrics classification diagram for various output types. assess the quality of reconstructed images or videos by comparing them to their respective originals. FID (Frechet Inception Distance)The Frechet inception distance (FID) is a metric used to measure the similarity between two sets of images. It is often used to assess the quality of images generated by generative adversarial networks (GANs), comparing the distribution of generated images to the distribution of real images. The lower the FID, the closer the two distributions, and therefore the better the GAN model. The FID is based on the Frechet distance between two multivariate Gaussian distributions. Given the two Gaussian distributions of real data (with mean \(\upmu\)1 and covariance \(\Sigma\)1) and generated data (with mean \(\upmu\)2 and covariance \(\Sigma\)2). Various image-related task models, such as DDPM [101], BigGAN [43], and Muse [150], as well as video-generation models, like PHENAKI [119], have utilized FID as a metric for their respective evaluations. Zero-Shot FID (Frechet Inception Distance)Zero-shot FID (Frechet inception distance) is a modified version of the FID metric that extends its application to evaluate the quality of images generated in a zero-shot setting. In the zero-shot scenario, the generated images do not belong to any predefined training classes or categories. Instead, they are produced based on textual descriptions or other forms of input that are distinct from the training data. This metric has been applied in various image synthesis techniques, including GLIDE [147] for image synthesis and IMAGEN Video [160] for video generation. Multi-Scale Structural Similarity Index Measure (MS-SSIM)The multi-scale structural similarity index measure (MS-SSIM) is a method for comparing the similarity between two images. It is an extension of the structural similarity index measure (SSIM), a popular metric for image quality assessment that is based on the degradation of structural information. While SSIM operates on a single scale, MS-SSIM evaluates similarity at multiple scales, which can provide a more robust and comprehensive measurement. This can be particularly helpful when dealing with images that have variations in size, resolution, or viewing conditions. The end result is a single value ranging from \(-1\) to \(1\), with \(1\) indicating perfect similarity, \(0\) meaning no similarity, and negative values suggesting inverse similarity. Several image synthesis models, including TAC-GAN [84], have utilized MS-SSIM as a means to evaluate the quality of synthesized images.