Wavelet Transform-Based Image Compression Code Implementation

This article presents a wavelet transform-based image compression code implementation. Wavelet transform is a signal processing technique that decomposes signals into different frequency subbands and time subbands, enabling better understanding of signal characteristics. In image compression applications, wavelet transform decomposes images into various frequency bands, allowing compression based on frequency characteristics to reduce storage requirements. Our implementation employs multi-level wavelet decomposition (using functions like wavedec2 in MATLAB) followed by threshold-based coefficient quantization and entropy encoding. The code provides comprehensive evaluation metrics including compression ratio calculation, execution time profiling, and Peak Signal-to-Noise Ratio (PSNR) computation for reconstructed images using inverse wavelet transform (waverec2). This implementation is applicable across various image processing domains such as digital photography, medical imaging, and drone imagery analysis, with configurable wavelet families (Haar, Daubechies) and threshold parameters for optimization.