Image Compression Using 9/7 Wavelet Transform

Image compression using the 9/7 wavelet transform represents a widely adopted methodology in digital signal processing. This approach decomposes images into frequency subbands through wavelet analysis, enabling effective removal of high-frequency components while preserving essential visual information. The implementation typically involves three key stages: wavelet decomposition using the 9/7 biorthogonal wavelet filters, threshold-based coefficient quantization, and entropy encoding. MATLAB provides comprehensive wavelet toolbox functions including wavedec2 for multi-level decomposition and waverec2 for reconstruction. Experimental simulations conducted on various test images validate that this method achieves substantial compression ratios while maintaining acceptable image quality. The 9/7 wavelet's excellent energy compaction properties make it particularly suitable for lossy compression applications. Therefore, employing 9/7 wavelet transform for image compression presents a technically sound and efficient solution for digital image processing systems.