Wavelet Transform-Based Image Compression Coding

This document presents a wavelet transform-based image compression coding methodology that utilizes various wavelet transform techniques to achieve a high compression ratio of 12:1. The wavelet transform serves as a mathematical tool that decomposes images into different frequency subbands, enabling more efficient compression through multiresolution analysis. The implementation typically involves several key algorithmic steps: First, the image undergoes discrete wavelet transformation using filter banks (such as Daubechies or Haar wavelets) implemented through convolution operations. This decomposition produces approximation coefficients (low-frequency components) and detail coefficients (high-frequency components) across multiple resolution levels. Following the transformation, critical compression operations include: Threshold-based quantization of wavelet coefficients where smaller coefficients (often representing noise or less visible details) are zeroed out, followed by run-length encoding and entropy coding (e.g., Huffman coding) for the remaining significant coefficients. The coding process selectively preserves perceptually important image characteristics while discarding redundant information. This wavelet-based compression approach finds widespread application in image processing domains, effectively reducing storage requirements and enhancing transmission efficiency. The method's superiority lies in its ability to maintain critical image features and details while achieving substantial size reduction, making it particularly suitable for applications like JPEG2000 and medical imaging systems where both compression efficiency and image quality preservation are paramount.