The `optimize=True` and `quality=60` parameters in the Pillow library are used for different purposes when optimizing images:
1. optimize=True:
- This parameter tells Pillow to optimize the image file size by applying lossless compression techniques.
- Lossless compression means that the image quality is not reduced, but the file size is decreased by removing unnecessary metadata and optimizing the image data.
- When you use `optimize=True`, Pillow will analyze the image and apply the best lossless compression techniques to reduce the file size without losing any image quality.
2. quality=60:
- This parameter sets the quality level for the image when saving it.
- The quality value ranges from 0 (worst quality, smallest file size) to 100 (best quality, largest file size).
- When you use `quality=60`, Pillow will apply lossy compression to the image, which means that some image quality will be sacrificed in order to achieve a smaller file size.
- Lossy compression is typically used for JPEG images, as it can significantly reduce the file size while maintaining a good level of visual quality.
In summary:
- `optimize=True` focuses on lossless compression to reduce the file size without losing any image quality.
- `quality=60` applies lossy compression, which reduces the file size by sacrificing some image quality.
You can use these two parameters together to achieve a balance between file size reduction and image quality preservation. For example, you can first use `optimize=True` to apply lossless compression, and then use `quality=60` to apply additional lossy compression if the file size is still too large.
The optimal settings for `quality` will depend on your specific use case and the desired balance between file size and image quality. Experimenting with different quality levels can help you find the best compromise for your needs.
Citations:[1] https://www.holisticseo.digital/python-seo/image-optimization/
[2] https://flowygo.com/en/blog/pillow-optimize-images-with-python/
[3] https://pypi.org/project/optimize-images/
[4] https://github.com/topics/image-optimizer?l=python
[5] https://cloudinary.com/blog/image_optimization_in_python