Research On Image Compression Based Wavelet Transform

Image compression technique plays an important role in efficient transmission and storage of multimedia data. Therefore, academic and engineering circles have been paying much attention to the research and development of image compression for many years. Because of its multiresolution properties, wavelet transform has been successfully applied as the decorrelation transform in image compression. Since wavelet is still in its childhood, however, its power in the image compression application field is far from been fully expoited.The thesis introduces the basic concepts of wavelet transform and multiresolution analysis. Then the second generation wavelets and its construction scheme ?lifting scheme ?are presented. With the help of lifting scheme, wavelet transforms that map integers to integers and morphological wavelet are built up.The main works and conclusions in this thesis are listed as follows:? The selection of wavelet basis.Because different images have different characteristic properties, it is worthy to do research on how to select wavelet basis for the compression of a specific image. A novel image compression algorithm, which adaptively takes reversible integer-to-integer wavelets as the decorrelation transform depending on the value of entropy of image, is presented. The experimental results show that the algorithm can achieve higher lossless compression ratio than the classical S+P transform in most cases.? The modeling of the wavelet coefficients.Three typical coding schemes based on wavelet transform are analyzed. Both EZW and SPIHT utilize the correlation of different sub-band to construct a quad-tree, which is a variation of the classical run-length-coding, while EBCOT makes use of the correlation of neighbors in the same sub-band to organize context for high order adaptive arithmetic coding. Finally, we propose an algorithm for image compression based on the partial ordering zero-tree and the approximate Markov significance of neighbors, which makes full use of the properties of the coefficients of wavelet transformation. The experimental results demonstrate that in the case of lossless compression, the algorithm achieves higher compression ratio than the three coding schemes.