Image Quality Analysis

This repository provides a comprehensive framework for image preprocessing, compression, and quality evaluation. The core focus is on:

• Entropy-based texture analysis
• Histogram equalization
• Linear predictive modeling
• JPEG compression
• Downsampling and filtering
• Quality evaluation using SSIM (Structural Similarity Index) and PSNR (Peak Signal-to-Noise Ratio)

Contents

• project3.ipynb: Python-based notebook for image processing and metric evaluation.
• image_analysis.m: MATLAB script that implements all image analysis stages step-by-step.
• Sample images (not included): sunset_sparrow.jpg, sunset_sparrow.png, lakebuilding.bmp.

Key Techniques

• RGB to Grayscale conversion
• Histogram Equalization
• Entropy Calculation
• Linear Prediction Coefficients (AR Modeling)
• Downsampling via imresize
• JPEG Compression with variable quality factors