Image Compression Using Wavelet Transform

This document discusses image compression using wavelet transforms. Wavelet transform serves as an effective method for image compression by decomposing images into wavelet coefficients at different frequencies. This approach reduces storage requirements while preserving critical image features. In practical implementations, the process typically involves three main stages: wavelet decomposition using functions like wavedec2 in MATLAB (for 2D signals), coefficient thresholding to eliminate insignificant values, and image reconstruction through inverse wavelet transforms. The algorithm leverages wavelet properties such as multi-resolution analysis and energy compaction to achieve superior compression ratios compared to traditional methods like DCT. Key parameters include wavelet type selection (e.g., Haar, Daubechies), decomposition level, and thresholding strategies (hard/soft thresholding). Wavelet transforms are widely adopted in image processing due to their ability to maintain better image quality at higher compression rates, making understanding and mastering wavelet-based compression techniques essential for modern image processing applications.