Quantization Techniques in Python

This repository demonstrates two practical applications of quantization:

1. Image Quantization: Reducing color depth of an image.
2. Model Quantization: Compressing a TensorFlow model using TFLite for edge deployment.

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📁 Files

File	Description
quant_image.py	Performs image color quantization from 256 to N levels.
quant_model.py	Applies post-training quantization to a Keras model.
requirements.txt	Required Python packages for running the scripts.
results/	Contains output image from quantization.

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🧪 1. Image Quantization

📌 Purpose

Reduce an image’s color depth (e.g., from 256 to 16 levels per channel) and observe visual tradeoffs.

🚀 How to Run

python quant_image.py

Make sure your_image.jpg is present or modify the script to use another image. 💾 Output quantized_output.png

🤖 2. Model Quantization (TensorFlow)

📌 Purpose

Use TensorFlow Lite to compress a Keras model for edge devices.

🚀 How to Run

python quant_model.py

📦 Installation

Install dependencies:

pip install -r requirements.txt If you're using a virtual environment:

python3 -m venv venv source venv/bin/activate pip install -r requirements.txt

📚 References

TensorFlow Lite Quantization Matplotlib