A study on optimal Image Data Multiresolution Representation and Compression Through Wavelet Transform

Wavelet 변환을 이용한 최적 영상 데이터 다해상도 표현 및 압축에 관한 연구

This paper proposed signal decomposition and multiresolution representation through wavelet transform using wavelet orthonormal basis. And it suggested most appropriate filter for scaling function in multiresoltion representation and compared two compression method, arithmetic coding and Huffman coding. Results are as follows 1. Daub18 coefficient is most appropriate in computing time, energy compaction, image quality. 2. In case of image browsing that should be small in size and good for recognition, it is reasonable to decompose to 3 scale using pyramidal algorithm. 3. For the case of progressive transmittion where requires most grateful image reconstruction from least number of sampls or reconstruction at any target rate, I embedded the data in order of significance after scaling to 5 step. 4. Medical images such as information loss is fatal have to be compressed by lossless method. As a result from compressing 5 scaled data through arithmetic coding and Huffman coding, I obtained that arithmetic coding is better than huffman coding in processing time and compression ratio. And in case of arithmetic coding I could compress to 38% to original image data.