基于方向性小波子树分形图像编码的改进算法

本文在分析混合分形零树小波图像编码算法(FZW)优缺点的基础上，提出一种新的基于方向性小波子树的分形图像编码算法。该算法结合零树小波编码和分形编码，通过在匹配搜索过程中使用方向性range和domain子树，提高匹配精度，改善了传统分形小波图像压缩中的方块效应，更大限度的保留了图像的边缘信息。实验结果表明，该算法在提高压缩比和去除图像的方块效应方面，均取得了良好的效果。     

基于方向性零树小波的分形图像编码

该文阐述了分形图像方块编码在小波变换域内实现的原理,将零树概念扩展后与小波域的分形仿射变换相结合,提出一种基于方向性零树小波的分形图像编码方法。计算机模拟实验表明,在高压缩比时,其编码性能优于典型的基于四叉树分割的分形图像编码性能,接近于嵌入式零树小波编码（ＥＺＷ）的性能。     

基于方向性零树小波的分形图像编码

该文阐述了分形图像方块编码在小波变换域内实现的原理,将零树概念扩展后与小波域的分形仿射变换相结合,提出一种基于方向性零树小波的分形图像编码方法。计算机模拟实验表明,在高压缩比时,其编码性能优于典型的基于四叉树分割的分形图像编码性能,接近于嵌入式零树小波编码(EZW)的性能。     

基于Contourlet变换的自适应门限分形零树混合图像编码

Contourlet变换的基函数分布于多尺度、多方向上,弥补了小波基函数方向性不足的缺陷.本文考虑了分形编码过程中子树与父树匹配误差门限对编码的影响,提出了一种基于Contourlet变换的自适应门限分形零树混合图像编码方法,给出了所提出方法的实验结果,并与基于小波变换的分形零树混合图像编码方法进行了比较.实验结果表明,在压缩比相同的情况下,本文方法的PSNR性能优于基于小波变换的分形零树混合图像编码方法,能很好地保留图像的纹理和轮廓.     

基于BWT和FVQ的极低比特率图像编码算法

该文提出了一种基于双正交小波变换（ＢＷＴ）和模糊矢量量化（ＦＶＱ）的极低比特率图像编码算法。该算法通过构造符合图像小波变换系数特征的跨频带矢量,充分利用了不同频带小波系数之间的相关性,有效地提高了图像的编码效率和重构质量。     

一种基于双正交小流变换和格型矢量量化的视频编码算法

本文提出了基于双正交小流变换和格型矢量量化的视频编码算法，在该方案中，小波变换将图像分解成多分辩率的子带图像，多分辩率运动估值技术实现子带图像的帧间预测，格型徉量量化对预测差值子带图像进行编码，从而获得了性能较好的活动图像编码新算法。     

基于分形编码的图像相似匹配研究

本文以自适应四叉树分割的分形图像编码为基础，研究了极坐极表达下分形码之间的匹配，并在此基础上提出了图像间分形码峰值信噪比的概念和算法，以达到量化图像间相似性的目的，实验证明，这种基于自适应分形编码的图像相似性匹配方法 人的主观判断非常接近，非常适合于用来构建和管理大图像数据库。     

基于分形与改进的SPIHT算法的图像压缩方法

为了避免分形编码所固有的方块效应,进一步提高图像编码的工作效率和重构图像的质量,对分形编码和小波零树编码进行优化组合,提出一种分形与改进的SPIHT算法相结合的图像压缩方法.基本方法是,对小波分解后的低频子带进行基于信息熵的快速分形编码,以减少编码时间;时包含图像细节边缘信息的高频子带进行改进的SPIHT编码,以舍去算法中对显著系数的排序扫描过程,减少算法的复杂度,同时提高重构图像的峰值信噪比.实验表明,相对于经典分形缟码和小波城内的分形编码,该方法在相同压缩比下,提高了编码效率和重构图像的质量,是一种高效快速的编码方法.     

适宜图像编码的双正交小波基的选择

提出一种新的选择适合图橡编码应用的双正交小波基的方法。通过用几个已证实是与图像编码密切相关的小波基评价标准对小波进行全面评价，选出了一些适宜图像编码应用的双正交小波基，并作了编码模拟检验。     

一种基于小波变换的遥测图像编码新方法

为了实现火箭遥测图像的高效处理,优化火箭遥测图像的编码流程,提出了一种基于小波变换的火箭遥测图像编码新方法。在图像小波变换的基础上,以空间方向树为编码单位,采用改进的分层树集合分割(SPIHT)算法实现图像重要小波系数的比特平面编码,并采用联合码率优化截取方法,优先截取和传输重要比特平面的遥测图像数据。通过系统测试,结果表明：与传统的火箭遥测图像编码方法相比,新的遥测图像编码方法具有丢帧率低、重构图像质量好、编码效率高等优点,满足运载火箭遥测图像处理和传输的工程需求。     

基于分形和小波变换的自适应混合图像编码

待编码图像经过金字塔型离散小波变换后的系数在小波域内可以组成分层树状数据结构一个小波树，这些跨越不同分辨率的小波树之间存在一定的相似性，可以通过分形变换来描述，本文正是构造小波树的基础邮基于分形和小波变换的自适应混合图像压缩算法，实验证明，我们提出的图像压缩方法与ＪＰＥＧ相比，能够在相近的压缩比的情况下（６０：１～７０：１）使得重建图像的ＰＳＮＲ（〉２９．５ｄＢ）增加约５．４ｄＢ并且图像的主观视觉     

一种基于混合变换的分层图像表示与编码

提出一种基于混合变换的分层图像编码算法，它将原始图像划分成包含不同结构特征的图像平面，如平滑边缘层和纹理层等等，然后采用不同的压缩算法对不同的图像层进行编码，从而达到对整幅图像最优编码的目的。本文实际上给出了一种新的编码框架，在这个框架内综合了不同的信号变换方式，将它们有机结合起来能够提供一种有效的图像表示方式。实验结果表明，与采用单一小波图像编码算法相比，本文算法能够获得更好的图像恢复质量。     

Embedded image compression based on wavelet pixel classification and sorting

The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

基于能量树编码的小波图像压缩算法

针对小波变换的空间能量聚集特性,提出了一种基于能量树编码的小波图像压缩算法。该算法在离散小波变换的基础上,分别对图像的各高频子带按其局部能量构建分层能量树,利用总能量和各层的能量角等效表示子带的小波系数;根据给定的压缩比,选择合适的代价函数构建最佳能量树,然后对其进行量化和编码,通过自适应的比特率分配实现小波图像压缩。实验结果表明,该算法实现简单,重构图像质量好,与当前多种主流的小波图像压缩算法相比,压缩性能有了明显提高。     

Edge-preserving image compression using adaptive lifting wavelet transform

In this paper, a novel 2-D adaptive lifting wavelet transform is presented. The proposed algorithm is designed to further reduce the high-frequency energy of wavelet transform, improve the image compression efficiency and preserve the edge or texture of original images more effectively. In this paper, a new optional direction set, covering the surrounding integer pixels and sub-pixels, is designed. Hence, our algorithm adapts far better to the image orientation features in local image blocks. To obtain the computationally efficient and coding performance, the complete processes of 2-D adaptive lifting wavelet transform is introduced and implemented. Compared with the traditional lifting-based wavelet transform, the adaptive directional lifting and the direction-adaptive discrete wavelet transform, the new structure reduces the high-frequency wavelet coefficients more effectively, and the texture structures of the reconstructed images are more refined and clear than that of the other methods. The peak signal-to-noise ratio and the subjective quality of the reconstructed images are significantly improved.     

A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT

Due to its excellent rate–distortion performance, set partitioning in hierarchical trees (SPIHT) has become the state-of-the-art algorithm for image compression. However, the algorithm does not fully provide the desired features of progressive transmission, spatial scalability and optimal visual quality, at very low bit rate coding. Furthermore, the use of three linked lists for recording the coordinates of wavelet coefficients and tree sets during the coding process becomes the bottleneck of a fast implementation of the SPIHT. In this paper, we propose a listless modified SPIHT (LMSPIHT) approach, which is a fast and low memory image coding algorithm based on the lifting wavelet transform. The LMSPIHT jointly considers the advantages of progressive transmission, spatial scalability, and incorporates human visual system (HVS) characteristics in the coding scheme; thus it outperforms the traditional SPIHT algorithm at low bit rate coding. Compared with the SPIHT algorithm, LMSPIHT provides a better compression performance and a superior perceptual performance with low coding complexity. The compression efficiency of LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information belonging to the lower frequency bands earlier in the bit stream than that of the SPIHT to better exploit the energy compaction of the wavelet coefficients. HVS characteristics are employed to improve the perceptual quality of the compressed image by placing more coding artifacts in the less visually significant regions of the image. Finally, a listless implementation structure further reduces the amount of memory and improves the speed of compression by more than 51% for a 512×512 image, as compared with that of the SPIHT algorithm.     

一种基于信源划分定理的小波帧内编码方法

本文在SPIHT算法的基础上，基于信源划分定理提出了一种新的小波系数量化编码算法。这种算法更好的利用了小波系数的统计特性，可以获得比SPIHT算法更高的压缩效率，并且能够产生完全嵌入码流。实验表明，这种算法非常适合于极低码率视编码的帧内编码，在相同压缩比下，能够取得比H.263 帧内编码器更好的重建图像质量。     

基于LS9/7小波的图像压缩算法研究

提出了一种基于提升格式小波的SPIHT图像压缩算法,该算法采用LS9/7进行小波变换,然后采用SPI-HT算法对变换系数进行编码,可实现任意码率的压缩.该算法具有运算简单,编码速度快等优点.实验结果表明,设计的算法是一种有效的图像压缩算法,能实现图像的快速压缩编码,其图像编解码时间和重构图像质量均优于原算法.     

一种基于小波系数重要图编码的图像压缩算法

提出了一种新的基于小波系数重要图编码的图像压缩算法。该算法根据量化后的波系数的特点进行了一种期望排序，然后舍掉序列后面大量的零值小波系数，从而得到一个波系数子集，能以少的小波系数来很好地逼近原始图像，省去了零树编码中零树结构带来的大量比特开销。实验表明，该算法与MPEG-4的静止图像压缩算法相比较，重构图像的峰值信噪比（PSNR）值在相同码率下有较大的提高。