Contourlet-Based Image Compression Coding with Performance Metrics Analysis

This document explores contourlet-based image compression coding technology, which employs multi-scale and multi-directional subband decomposition to effectively capture image details. The core algorithm involves applying contourlet transform through pyramidal directional filter banks (PDFB), where the Laplacian pyramid first captures point discontinuities followed by directional filter banks linking these points into linear structures. We detail the workflow including: 1) Image decomposition using contourlet_transform() function with specified scale and direction parameters 2) Coefficient thresholding through hard/soft thresholding techniques 3) Entropy coding implementation using Huffman or arithmetic coding methods. Key performance metrics are systematically evaluated: PSNR (Peak Signal-to-Noise Ratio) calculated via psnr() function comparing original and reconstructed images, compression ratio derived from file size comparisons using getFileSize() method, and runtime measured with tic-toc commands. The discussion covers advantages like superior geometric representation compared to wavelets, alongside limitations including computational complexity. Comparative analysis with JPEG, JPEG2000, and wavelet-based compression demonstrates contourlet's effectiveness for images with rich textures and contours. Through this technical exploration, you'll gain comprehensive understanding of contourlet-based compression and practical implementation strategies for optimizing image compression performance.