【作者】 尹汪宏；

【导师】 李朝锋；

【作者基本信息】 江南大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 图像压缩是图像存储、传输的基础。原始图像数据存储空间较大,不利于存储、传输。为了减小图像数据的存储空间和通信带宽,实现数据实时处理、显示和传输,需要对原始图像进行高效压缩。图像压缩编码可分为无损压缩编码和有损压缩编码。经典压缩算法理论已比较成熟。近来,又出现新的基于小波变换的压缩方法、分形压缩方法和神经网络压缩方法,称为第二代图像压缩编码方法,其具有更高的压缩比和图像恢复质量,受到越来越多的重视,但由于理论还不完善,在实际应用中仍存在许多问题。针对图像压缩的现状,本文结合支持向量回归与小波理论对图像压缩进行了研究,主要工作如下:(1)研究了支持向量回归与DCT变换相结合的图像压缩方法,并通过实验论证了其可行性。(2)我们结合SPIHT结构,提出一种支持向量回归与小波变换相结合的灰度图像压缩方法。该方法先将原始图像进行离散小波变换,得到多分辨率小波图像,再通过SPIHT结构进行小波系数重组,然后对每棵树上的小波系数进行支持向量回归,获得一系列支持向量和相应的权值,通过较少的支持向量拟合原始小波系数而达到数据压缩目的。(3)研究了3DSPIHT的支持向量回归小波多光谱遥感图像压缩方法。在二维SPIHT支持向量回归小波图像压缩的基础之上,将压缩方法扩展到3DSPIHT,先对多波段光谱数据进行空间小波变换,再用3DSPIHT结构进行系数重组,然后通过支持向量回归进行图像压缩。实验结果表明,此方法可取得较好的效果,但压缩中,支持向量回归时间,压缩时间较长是一个有待解决的问题。更多还原

【Abstract】 Efficient encoding algorithm is the key for image storage and transmission. Original image data needs huge storage space, and is unfavorable to store and transmit.To decrease the image storage space and realize real-time data process, it needs a high-perfomance image compression algorithm. Image compression code is divided into loseless compression and lossy compression.The classical image compression algorithm has already been sucessfully developed. Lately, some new compression method is deeply researched, such as image code based on wavelet transform, fractal theory and neural networks, which is called as the second-generation image code method, and are becoming increasingly concerned as a result of its higher-quality image compression effect. But because of their faulty theory, there are still many defects in factual application. According to research states of image compression,we deeply research image compression algorithm based on support vector regression and wavelet transform, and the main works are described as following.(1) We deeply research an image compression algorithm of combining support vector regression and discrete cosine transform, and experimental results show its validity.(2) Combining SPIHT structure, we propose a gray image compression algorithm based on SVR and wavelet transform. First, Original image data is decomposed by wavelet transform to gain multiresolution image data. Secondly, wavelet coefficients are resorted by using the SPIHT structure, and then we use support vector regression to process these wavelet coefficients on each SPIHT to obtain a series of support vectors and their corresponding weight value. At last Less support vectors are used to fit the primitive wavelet coefficients, thus the target of data compression is achieved.(3) Combining 3DSPIHT structure, we research a multi-spectral remote-sensing image compression algorithm of combing SVR and wavelet transform. Based on image compression algorithm with two-dimensional SPIHT coefficient trees, we extend it to image compression algorithm with 3DSPIHT. The multi-spectral remote-sensing image are decomposed by two-dimensional wavelet transform, and then their wavelet coefficients are reorganized and resorted by 3DSPIHT structure, and lastly are compressed by support vector regression. Experimental results show this method may gain preferable effects. But in Data Compression, longer processing time of support vector regression and image compression need be solved in future research.更多还原