## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.1. **Evaluation Metrics for Image Processing:**
AccuracyAccuracy is a metric commonly used in machine learning and classification tasks. It measures the correctness of predictions or classifications made by a model. It is calculated as the ratio of the number of correct predictions to the total number of predictions. Accuracy is often expressed as a percentage. In the context of image-related tasks, accuracy is a commonly used metric to assess the performance of models, such as domain transfer network (DTN) [89] and TGAN [67], for tasks like image classification, image segmentation, or object detection. F1 ScoreThe F1 score is a measure of a model's accuracy that takes into account both precision and recall. It is commonly used in machine learning and statistical analysis to evaluate the performance of classification models, particularly in imbalanced datasets where one class may dominate the others. The F1 score is calculated as the harmonic mean of precision and recall: F1 score = 2 * (precision * recall)/(precision + recall). Learned Perceptual Image Patch Similarity (LPIPS)Traditional metrics like SSIM and PSNR were effective at measuring numerical image similarity, but they are not aligned with human visual perception. To improve this, LPIPS was introduced to better model human visual perception. DiffusionCLIP [131] and DIFFEDIT [164] are two models we discussed in our paper, and we have chosen one of their metrics as LPIPS to evaluate images manipulated by textual prompts. MAE (Mean Absolute Error)Mean absolute error (MAE) is generally used for regression tasks, but it can also be used as a loss function for image reconstruction, like manipulation of images or editing images. One such model, called DiffusionCLIP [131], uses MAE to evaluate reconstructed image quality. A lower MAE indicates a better reconstruction of the image. Directional CLIP Similarity (Sdir):This metric is used for evaluating the performance of text-driven image manipulation changes among the two images in a CLIP space. A higher Sdir score indicates the two images were more consistent, and text-based edit instructions have been successfully carried out by the model; DiffusionCLIP [131] is one such model that uses Sdir as one of its evaluation metrics. _L2 Error:_ L2 error in the context of image manipulation is used as a component of the loss function to measure the difference between the generated image and the target image so that the generated image to be closed as possible to the target image in the pixel space. _Dice:_ It is also known as the dice similarity coefficient and is used for evaluation of segmentation by generative AI models. One such model, called SegAN [100], which is useful in medical applications to segment the X-ray scans of patients, is now used to evaluate these segmentations. The dice score is 1 for perfect segmentation and 0 for completely non-overlapping masks. _Precision:_ In the context of image segmentation, precision is the proportion of true positive pixels, which indicates whether the pixels were identified as belonging to a certain class out of all pixels that were identified as belonging to that particular class. A higher precision indicates fewer false positives. _Sensitivity:_ Is also known as recall or true positive rate, which measures the proportion of true positive pixels to actual positive pixels. SegAN [100] used sensitivity as one of the metrics to evaluate the segmentation performed by the model. _PSNR (Peak Signal-to-Noise Ratio):_ PSNR is one of the metrics used to measure the quality between the original image and the deblurred image. In the context of image deblurring, DeblurGAN [61] and DeblurGAN-v2 [56] use PSNR as one of their metrics to evaluate the image reconstruction quality. _Normalized Root Mean Square Error (NRMSE):_ The normalized root mean square error (NRMSE) is a performance metric used to evaluate the accuracy of predictive models, particularly when numerical outputs are involved. It is essentially the root mean square error (RMSE) adjusted for scale, which makes it useful for comparing datasets.