Research Of Image Compression Based On Bi-orthogonal Wavelet Packet

The wavelet transformation can provide a good mechanism to explore human visual system, while it removes the data correlation in image. So the image compression based on wavelet transformation has became researching-hotspot in recent years. In this dissertation, image compression method and improved method based on bi-orthogonal wavelet packet was discussed in detail.Firstly, this paper introduced theory of the wavelet analysis in detail, fetched algorithm of decompose and compose of bi-orthogonal wavelet packets, including transform of wavelet and important quality, multi-resolution analysis, two-dimension Mallat fast algorithm of decompose and compose, algorithm of the wavelet packets.Then, some critical particulars was research based on the wavelet static image compression, including grade of quality of image, choice of filter, the confirmation of decomposition stages, system of the wavelet packets image compression, question of boundary, etc. as well, a new method is presented based on improved two-local threshold algorithm-the wavelet coefficients' magnitude and spatial regularity, and gives the concrete scheme.Finally, experiments included the selection of best bi-orthogonal wavelet basis, the confirmation of decomposition stages, image compression and analysis based on improved threshold method. Statistics shows the improved method not only can obtain the best PSNR but can obtain high ratio-compression.Through extensive experiments, the results show that the new compression method is more effective than the others. It improves the compression ratio by 2 times. The human's visual effects of the reconstructed image are close to general algorithms'. In addition, the new compression method is simple, and the memory requirement in the operation process is lower, and it is fit for parallel optimizing progress. The compression speed can be advanced by using DSP, and it also can be implement image real time compression with little information loses.