Research And Application In The Field Of Image Compression By Wavelet Transform

Since the birth of wavelet analysis in the 1980s of the 20th century, it is developing rapidly. Especially after the theories of construction of compactly-supported orthogonal wavelet, multi-resolution wavelet analysis and fast wavelet transform were proposed, the research concerning the wavelet continues to make major breakthroughs during the subsequent 10 years. As wavelet analysis can make multi-resolution analysis for the interesting part of the signal, known as signal microscope. Wavelet analysis has become one of the fastest and most spectacular subjects almost all the subjects, and Involved in or applied to all the fields of information.Image compression plays an important role in multimedia technology, although the traditional image coding standard or compression methods are widely used, but several problems had not solved, such as low coding efficiency and low compression ratio etc. Image compression using wavelet has natural advantages such as high compression ratio, good progressive transmission and scalable quality control. Its principle is based on image features and the physiological characteristics of human vision, raw image is decomposed of high-frequency part and low-frequency part, low-frequency part represent profile of image, accordingly high-frequency part represent image details. Because the energy of image is concentrated mainly in the low-frequency part, through adopting of appropriate data encoding algorithm to compression the raw image.Nowadays, several influential codec algorithms of the wavelet coefficients has been proposed, such as EZW algorithm (Embedded Zero-tree Wavelet), SPIHT algorithm (Set Partitioning In Hierarchical Trees) and EBCOT algorithm (Embedded Block Coding with Optimized Truncation) etc. and the EBCOT algorithm as one of the core for image coding technologies have been adopted for the new generation image compression standard JPEG200, much of the trend to replace JPEG. In 1996, Said and Pearlman proposed the SPIHT algorithm, which is another effective method base on the Zero-Tree, the advantage is even without entropy coding (such as arithmetic coding) after Zero-tree encoding higher compression ratio can obtain. Comparing with the EBCOT algorithm, compression rate is almost similar, meanwhile the SPIHT algorithm has faster compression speed and the algorithm is simpler.This paper balance between the wavelet analysis theory and it's application in image compression, all contents are cross-linked together and gradually advancing, and propose an improved SPIHT algorithm which is based on denary quantization. Comparing with the classical SPIHT algorithm, the improved SPIHT algorithm is better for its lower codec times, comparative time consumption and PSNR under the premise of unchanging bits surface encoding and progressive transmission feature of classical SPIHT. Finally, a MATLAB program is proposed to prove the rationality of the improved SPIHT algorithm.