How to evaluate the quality of AI-generated images?

Evaluating the quality of AI-generated images involves several aspects, including visual quality, realism, diversity, and the accuracy of details. Here are some methods and criteria:

Visual Quality and Realism

1. Perceptual Quality: Assess how natural and visually appealing the image looks to human eyes. This can be subjective but can be guided by comparing with real images.

   • Example: Comparing a generated portrait to a real photograph to see if the skin tones, shadows, and textures look realistic.

2. Fidelity: Measure how well the AI captures the essence and details of the subject matter.

   • Example: If generating a landscape, check if the trees, mountains, and sky look like they belong together in a coherent scene.

Diversity

• Variety in Outputs: Ensure that the AI can produce a range of images for the same prompt, showing creativity rather than repetitive patterns.
  • Example: Generating multiple images of a cat and observing if they all look like different cats rather than slight variations of the same one.

Accuracy of Details

1. Semantic Consistency: Verify that the AI maintains the logical relationships and details within the image according to the input prompt.

   • Example: If prompted for "a cat playing with a ball," check if both elements are present and interacting as described.

2. Technical Metrics: Use quantitative measures like PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and LPIPS (Learned Perceptual Image Patch Similarity) to compare generated images with reference images.

Tools and Services

For more advanced evaluation, especially in professional settings, cloud-based AI platforms can offer specialized tools:

• Tencent Cloud AI Image Generation Service: Provides not only image generation capabilities but also tools for assessing and refining the quality of these images. It leverages deep learning models to ensure high-quality outputs tailored to specific needs.

Using such platforms can streamline the evaluation process by offering automated assessments and detailed analytics on image quality, helping developers and artists refine their AI models for better results.

Remember, the best approach often combines both human judgment and automated metrics to get a comprehensive understanding of the image quality.