Composite-Image-Evaluation

Here are some possible evaluation metrics to evaluate the quality of composite images from different aspects.

• Evaluate whether the foreground is harmonious with background.

  • Harmony score: use illumination encoder to extract the illumination codes from foreground and background, and measure their similarity.

  • Inharmony hit: use inharmonious region localization model to detect the inharmonious region, and calculate the overlap (e.g., IoU) between detected region and foreground region.

• Evaluate whether the foreground object placement is reasonable.

  • OPA score: use object placement assessment model to predict the accuracy of object placement.

• Evaluate whether the foreground is compatible with background in terms of geometry and semantics.

  • FOS score: use foreground object search model to calculate the compatibility score between foreground and background in terms of geometry and semantics.

• Evaluate the fidelity of foreground, i.e., whether the synthesized foreground is similar to the input foreground.

  • Clip score: use CLIP to extract the embeddings from the input foreground image and the generated foreground patch, and measure their similarity.

  • Dino score: use DINO to measure the average cosine similarity between the input and generated foreground.

• Evaluate the over quality of foreground or the whole composite image.

  • FID: use pretrained image encoder (e.g., InceptionNet, CLIP) to extract the embeddings from real images and generated images, and measure their Fréchet Inception Distance.
  • QS: use quality score to measure the quality of each single generated image, and compute average score.

Some evaluation metrics have been integrated into our Libcom toolbox. Try this online demo for image composition and have fun!

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