# Introduction This lab demonstrates how to use KBinsDiscretizer from the Scikit-learn library to perform vector quantization on a sample image of a raccoon face. Vector quantization is a technique to reduce the number of gray levels used to represent an image. We will use KBinsDiscretizer to perform vector quantization on the raccoon face image. We will use 8 gray levels to represent the image, which can be compressed to use only 3 bits per pixel. We will compare the uniform and k-means clustering strategies to map the pixel values to the 8 gray levels. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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