MATLAB Implementation for Image Processing: Calculating MSE, SNR, PSNR, and Image Quantization

This image processing implementation focuses on calculating critical quality assessment metrics and performing image quantization. The key computations include Mean Square Error (MSE) to measure the average squared difference between original and processed images, Signal-to-Noise Ratio (SNR) to evaluate signal quality relative to noise, and Peak Signal-to-Noise Ratio (PSNR) which uses the maximum possible pixel value as a reference for quality measurement. Additionally, the implementation handles image quantization, which reduces the number of colors in an image by mapping pixel values to a limited set of quantization levels. For code implementation, the algorithm typically involves: - Reading and converting images to appropriate data types using imread() and im2double() - Calculating MSE using element-wise subtraction and mean squared operations - Computing SNR and PSNR through logarithmic transformations of variance ratios - Implementing quantization using round() or floor() functions with level scaling - Key functions include: imquantize() for controlled quantization, mean() and std() for statistical calculations, and log10() for dB conversion in SNR/PSNR computations The implementation ensures accurate metric calculations while providing efficient quantization with configurable bit-depth levels for different application requirements.