The Second Generation Wavelet-based Image And Video Compression

Image and video are among the most important information carriers for human being. Recently, the expansion of Internet and the development of multimedia technology have made it possible to implement new applications and services of multimedia. Although the Internet bandwidth and storage capability increased dramatically in the past decades, digitalized image and video signals still great challenges to our technology. As a feasible solution, data compression technology has been paid more and more attentions.In this paper, we use Daubechies orthogonal filter to obtain better image compression results than orthogonal wavelet. In detail, we use the Daub 9/7 wavelet converter along with Set Partitioning In Hierarchical Trees (SPIHT) and 3D-SPIHT algorithms. Describe the application of Embedded Zerotree Wavelet (EZW) algorithm and SPIHT algorithm in real world compression tasks. In practice, we implemented the SPIHT image compression algorithm and the 3D-SPIHT video compressing and decompressing algorithm.Introduce improved human visual characteristics into wavelet image coding algorithm. We use two algorithms to introduce human visual characteristics, the basic idea is to separate images into basic detailed or textured regions, smooth regions and strong regions by two threshold values T₁ and T₂ and then give different image parameter to corresponding wavelet coefficient. In the first algorithm, we introduce the BP neural network for the parameter estimation in Generalized Gaussian Distribution; In the second algorithm, we define T₁ the average value of all entropys and T₂ a smaller image deviation. We also use fuzzy algorithm in the dividing position of three different image blocks to make the division of image blocks more reasonable. Both of experiments show very inspiring results.