Wavelet Packet-Based Image Decomposition, Compression, and Reconstruction Using MATLAB

This article primarily demonstrates image decomposition, compression, and reconstruction using MATLAB's wavelet packet toolbox. The implementation begins with wavelet packet decomposition, which breaks down an image into subbands at different scales and orientations using functions like wpdec2() for 2D signals. This decomposition effectively captures detailed texture information and frequency components of the image. Subsequently, compression algorithms are applied to the decomposed subbands to reduce storage requirements and transmission bandwidth. Thresholding techniques (e.g., wthcoef() or custom quantization methods) can be implemented to eliminate insignificant coefficients while preserving critical visual information. Finally, wavelet packet reconstruction synthesizes the compressed subbands back into the original image using wprec2(), achieving high reconstruction quality. This approach maintains essential image characteristics while significantly improving storage efficiency and transmission performance. The article will detail methodological strategies and practical coding techniques for implementing wavelet packet-based image processing in MATLAB, including parameter selection criteria and performance evaluation metrics.