【作者】 董世都；

【导师】 杨小帆；

【作者基本信息】 重庆大学 ， 计算机软件与理论， 2003， 硕士

【摘要】 图像压缩在多媒体存储和传输中扮演十分重要的角色，小波变换因具有时域频域局部性、多分辨等性质在图像压缩中正在逐步取代DCT变换，并已得到广泛的应用。基于小波的图像压缩已成为图像压缩研究的主流，一些小波系数模型也随之产生，如EZW、SPIHT和用于JPEG2000的EBCOT，这三种模型都具有SNR可扩展性质，其中EBCOT还具有分辨率可扩展性质。这些可扩展性质对于在互联网上传送图像非常重要。上下文模型因能显著地提高图像的编码效率，在图像压缩中也得到广泛的应用。JPEG-LS和CALIC模型都利用了上下文模型，他们的平均无损压缩比高于EZW、SPIHT和JPEG2000。受Glicbawls、CALIC、ECECOW和 EZW编码方法的启发，本文提出了一种新的用于图像压缩的小波系数的上下文模型(PCW)。它通过量化当前系数的线性预测值形成上下文，把系数作为一个整体进行自适应的算术编码。实验结果表明，利用这种模型获得的平均无损压缩比高于SPIHT和用于JPEG2000的EBCOT。另外，这种模型充分利用了小波变换的多分辨率性质，具有分辨率可扩展性质，并且在每个分辨率下的压缩比也高于EBCOT。现在一些用于图像压缩的小波系数模型扩展到了三维小波，并应用于视频压缩。同样地，本文把经过修改的PCW也扩展到三维小波，提出了空间和时间分辨率可扩展的3D-PCW模型。它首先利用人眼视觉模型(HVS)对子带的系数进行量化；然后利用空间和时间相邻的系数对当前系数进行预测，以预测值的量化值作为上下文，对系数进行基于上下文的算术编码。由于3D-PCW把系数作为一个整体进行编码，每个系数只需要计算一次上下文，并编码一次，而EBCOT的扩展3D-ESCOT每个系数在每个平面上都需要计算一次上下文并编码一次，因而3D-PCW比3D-ESCOT算法的时间复杂度更低。实验数据也表明，对于每个量化幅度和每个空间及时间分辨率，3D-PCW不仅获得了比3D-ESCOT更高的压缩比，而且算法执行所需要的时间也更少。更多还原

【Abstract】 Image compression plays an important role in transmission and storage of multimedia. Wavelet has been widely adopted in image compression due to its multiresolution, time/frequency localization, lower time complexity, etc. With the advance of image compression techniques based on wavelet, a variety wavelet coefficient models are presented, such as EZW, SPIHT and EBCOT used in JPEG2000. They all are SNR scalable, and, especially EBCOT is resolution scalable. These properties are essential in transmission of image on Internet.Because the performance of an entropy coder can be significantly improved by having the coder dynamically adapt to the current "context", context models also have widely been adopted in image compression. Using context model, JPEG-LS and CALIC can improve their compression rate and gain higher lossless compression rate than EZW, SPIHT and EBCOT. Inspired by Glicbawls, CALIC, ECECOW, and EZW coding schemes, a new context model of wavelet coefficients for image compression is proposed. Wavelet coefficients are encoded by the arithmetic encoder, with the contexts being formed by quantizing linear prediction values. Experimental results show that the model achieves higher lossless compression rate of image than lossless SPIHT and lossless EBCOT used in JPEG2000. In addition, by exploiting the multiresolution property of wavelet, the model can deal with the transformed image for transmission purposes, which is resolution scalable, and earn higher compression rate for each scale of the image than EBCOT.Some coefficient models of wavelet for image compression have already been extended to video cases using Three-Dimensional wavelet. In the same way, after modification, PCW is also extended to 3D-PCW which is resolution scalable in time and space. First, the coefficients of wavelet are quantized by the threshold formed by the eye’s sensitivity to it’s subband. Then, Wavelet coefficients are encoded by the entropy encoder, with the contexts formed by quantizing their prediction values, which are determined by their neighbors in space and time dimensions. Since 3D-PCW tackles the coefficients as a whole, the context of a coefficient is computed only once, and the coefficient is encoded only once. But in 3D-ESCOT the context has to be computed once and the coefficent has to be encoded once on every bit plane. Therefore, 3D-PCW has much lower time complexity than 3D-ESCOT. Experimental results also show that for every quantization width, every scale in time and every scale in space, 3D-PCW not only achieves higher compression rate but also costs less time than 3D-ESCOT.更多还原