【作者】 黄同；

【导师】 樊延虎；

【作者基本信息】 延安大学 ， 信号与信息处理， 2007， 硕士

【摘要】 小波分析自20世纪80年代诞生以来发展迅速。特别是紧支撑的正交小波构建理论、多分辨率分析理论和快速小波变换算法提出后,在随后的这十多年中,有关小波的研究不断取得重大突破。由于小波分析可以对信号中感兴趣的部分进行多分辨分析,所以被誉为信号的显微镜。小波分析已经成为目前发展最快和最引人注目的学科之一,几乎涉及或者应用到信息领域的所有学科。图像压缩在多媒体技术中具有重要地位,传统图像编码压缩方法和标准虽得到广泛应用,但仍存在诸如编码效率低、压缩率低等诸多问题。小波用于图像编码压缩具有天然的优势,具有压缩率高、很好地渐进传输[1]、可伸缩的质量控制等优点。其原理是根据图像统计特征和人眼的生理特性[2],把图像分解为高频部分和低频部分,低频部分表示图像概貌,而高频部分表示图像细节。而图像能量主要集中在低频分量上,通过适当的编码算法达到数据压缩的目的。目前已经产生了多种有影响的小波系数编解码算法,如嵌入式零树算法(Embedded Zero-tree Wavelet),SPIHT算法(Set Partitioning In Hierarchical Trees)、EBCOT算法(Embedded Block Coding with Optimized Truncation)等,其中以EBCOT算法为核心之一的图像编码技术已经被采纳为新一代图像编码压缩标准JPEG2000,大有取代JPEG的趋势。而1996年Said和Pearlman提出了SPIHT算法,也是一种很有效的零树编码方法,其优点是零树编码结束后,即使不经过熵编码(例如算术编码)也能获得较高的压缩比,和EBCOT算法相比,压缩率几乎差不多,但压缩速度快,算法简单。本文兼顾小波分析的原理及其图像编码压缩中的应用,内容环环相扣,渐次推进,提出一种基于十进量化的SPIHT改进算法。该算法较经典SPIHT的最大优点是:在不改变SPIHT算法比特面编码和支持渐进传输特性的前提下,编解码次数大幅度降低,时间消耗和PSNR和标准SPIHT算法相当。最后给出MATLAB实现程序证明该改进算法的合理性。更多还原

【Abstract】 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.更多还原