一种具有抗误码性能的树块压缩算法

星载图像编码系统要求图像压缩算法具有一定压缩比、低复杂度的特点,同时希望尽可能减少码流比特错误造成的误码扩散。为了减少误码扩散,提出了一种易于硬件实现、低复杂度、抗误码强的树块数据无损压缩算法。图像数据经过小波变换后,将各级分辨率的系数按照树的结构进行排序,并由若干棵相邻的树构成树集合,称之为树块,每个树块独立进行比特平面编码,产生的码流独立进行打包。结果表明,这种编码方法有较低复杂度,易于硬件实现,能够有效优化,使输出码率达到最短,而且可以有效提高系统的抗误码性能。     

基于多描述和不等差错保护的航空遥感图像传输方法

 本文提出了一种针对航空遥感图像传输的信源信道联合编码方法.将小波变换后的图像进行小波树分组以形成多描述,并重复描述重要的低频子带系数;然后利用改进的多级树集合分裂(SPIHT)算法对每个描述单独编码,并为其提供不等差错保护.为保证编码实时性,提出了一种快速的码率分配搜索算法.仿真结果表明该方法在频率选择性莱斯衰落信道下实现了遥感图像的鲁棒传输,且具有较低的复杂度.     

基于加权小波的三维网格多描述编码

鉴于网格小波系数特点与图像小波系数的差异性,提出一种基于加权小波系数的多描述编码(WW_MDC)方法.利用小波变换,提取模型最粗糙网格和小波系数,对小波系数进行加权处理,使其更加适合随后的SPIHT算法,利用SPIHT码流分层内嵌特性将小波系数分组形成多个描述,可以很好的适应网络带宽的变化和丢包率(PLR)的变化.试验结果表明,该方法具有较高的压缩率和抗误码能力,并且相比较其他三维模型的多描述编码方法,该方法具有更小的重建误差.     

一种基于混合整型变换和3D-SPIHT的高光谱图像嵌入式无损压缩方法

利用高光谱图像空间、谱间相关性不同的特点,本文提出了一种基于混合整型变换和三维分层树集合划分算法(3D-SPIHT)的高光谱图像无损压缩方法,首先将波段进行分组,针对每一分组,采用不同的整型变换技术去相关,然后对变换系数进行系数重组,采用高效的基于小波系数特点的3D-SPIHT编码方法得到嵌入式码流,具有一定的抗误码性能.实验结果表明,该方法能够有效的去除高光谱图像的空间和谱间相关性,与现有的国际标准JPEG-LS、JPEG2000和基于三维离散余弦变换(3D-DCT)或三维离散小波变换(3D-DWT)的编码方法相比,压缩后的平均比特率均有明显降低.     

一种二进制算术码的软解码算法

算术码是一种高效率的熵编码方法,广泛应用在多媒体压缩中.但是,算术码的高压缩性能也导致其对信道传输中残存的误码极其敏感,存在误码扩散现象.本文将算术码解码过程表示为一个有限状态机(FSM),利用最大后验概率估计准则(MAP)进行序列解码,FSM提供先验信息估计和差错检测功能.通过对独立信源和JPEG2000编码图像的实验表明,该解码算法降低了二进制算术码的序列差错率,提高了解压缩图像的质量.     

高分辨率航测CCD图像的无损压缩算法

针对超高分辨率CCD相机拍摄图像,提出了一种无损压缩算法.算法以嵌入式小波零树编码为基础, 通过对零树编码输出的比特平面采用变长游程编码方法很好地提高了图像压缩比.同时,算法将游程编码与比特平面编码统一进行,编码效率大大提高.实验结果表明,该算法的像素平均比特率相对经典的嵌入式小波零树算法均有明显降低.     

一种新颖实用的交互式视频抗误码方法

本文提出一种新颖的交互式视频抗误码方法.首先,分析了块匹配运动补偿编码方法产生时域误码扩散的机理,然后,相应地提出了一种新颖的误码掩盖和防止误码扩散联合方法.实验结果表明,使用本文提出的交互式视频抗误码方法能够有效地抑制和防止误码的扩散,保证了恢复视频质量.另外,该算法与H.263+标准兼容,具有实用价值.     

基于分类的小波—分形混合编码

本文在小波变换域内利用小波系数的相似性,提出了基于分类的混合编码方法,对于不同类的小波树采用不同的编码方法。实验结果表明本算法的性能优于PPC、SQS等混合编码方法。     

一种新颖的矩形块-零树编码方法

提出了一种新颖的矩形块-零树编码方法,它采用零树编码方法结合了矩形块填充编码的思想对图像数据进行压缩编码。利用图像小波系数在幅值及相关性上具有的特点,对第3层小波系数采用矩形块填充法原则来组合一系列粗壮的树根,并由此延伸成一棵棵矩形块-零树,然后参照零树法对矩形块-零树处理,剩下的小波系数则按照零树法处理。实验证明,它是一种具有良好性能和较小计算量的图像压缩编码方法。     

一种改进的图像自适应零树编码方法

在研究Shapiro零树图像编码方案的基础上,提出了一种改进型的自适应嵌入式零树编码方法.本算法利用自适应的小波系数,增加了编码过程中零树个数,提高了编码效率,使在相同压缩率情况下,提高PSNR约为0.1~0.6dB,同时该编码方案仍保持零树编码产生嵌入式码流、支持多码率解码的特点.     

一种结合Contourlet和小波变换的图像编码算法

该文提出了一种Contourlet变换和小波变换相结合,使用新的空间方向树的类似SPIHT编码算法。该算法先对图像进行Contourlet变换,再对变换后的低频子带进行多级小波变换,然后根据变换后系数的结构特性,借鉴小波SPIHT编码思想,构造了一种新的空间方向树,实现了对变换后系数的类似SPIHT编码。仿真实验结果表明,该算法与小波变换,Contourlet变换和基于小波的Contourlet变换的SPIHT算法相比,重构图像保留了更多的纹理和细节信息,并且在低比特率下具有较高的峰值信噪比。     

Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.     

基于小波二重树编码的彩色图像压缩

该文提出了一种利用小波变换对彩色图像进行编码的算法。该算法在对Y,Cr,Cb分量进行小波分解的基础之上,结合人眼视觉系统特性对各子带进行量化,采用全新的二重树编码,有效地对高频子带进行压缩。实验结果表明,本算法实现简单,可达到很好的压缩效果。     

Hierarchical dynamic range coding of wavelet subbands for fast and efficient image decompression.

An image coding algorithm, Progressive Resolution Coding (PROGRES), for a high-speed resolution scalable decoding is proposed. The algorithm is designed based on a prediction of the decaying dynamic ranges of wavelet subbands. Most interestingly, because of the syntactic relationship between two coders, the proposed method costs an amount of bits very similar to that used by uncoded (i.e., not entropy coded) SPIHT. The algorithm bypasses bit-plane coding and complicated list processing of SPIHT in order to obtain a considerable speed improvement, giving up quality scalability, but without compromising coding efficiency. Since each tree of coefficients is separately coded, where the root of the tree corresponds to the coefficient in LL subband, the algorithm is easily extensible to random access decoding. The algorithm is designed and implemented for both 2-D and 3-D wavelet subbands. Experiments show that the decoding speeds of the proposed coding model are four times and nine times faster than uncoded 2-D-SPIHT and 3-D-SPIHT, respectively, with almost the same decoded quality. The higher decoding speed gain in a larger image source validates the suitability of the proposed method to very large scale image encoding and decoding. In the Appendix, we explain the syntactic relationship of the proposed PROGRES method to uncoded SPIHT, and demonstrate that, in the lossless case, the bits sent to the codestream for each algorithm are identical, except that they are sent in different order.     

基于3D-SPIHT的立体视频图像压缩编码

该文提出一种新的立体视频编码方案:在辅助序列中进行视差补偿预测和三维等级数集合分区(3D-SPIHT)编码,3D-SPIHT算法建立在真三维小波分解基础上,通过定义一种新的时空方向树结构,实现了静止图像SPIHT算法的三维扩展,实验结果表明该方案的编码性能略高于传统方案,具有较低的计算复杂度,所产生的嵌入式辅助序列码流,可根据通道带宽自适应调整输出码率,最大限度地提高辅助序列的质量。     

基于双向运动补偿的三维小波变换方法

提出了基于双向运动补偿的三维小波变换方法,与以往的三维小波变换方法相比较,该方法在三维小波视频编码方法中应用了双向的运动估计/补偿,进而实现了两种时域可扩展方式的组合,提高了视频编码的可扩展性。     

基于三维子波变换和分级零树扫描的视频编码算法研究

本文研究了三维子波变换和分级三维零树混合视频编码算法,对图像序列体作３层２２子带时,空分解后,再对最低频子带进行４子带空间分解,对这个特殊分解的三维子波结构,推广了零树的概念,用ＨＶＳ量化模型对子波系统施加量化,用分级三维零树表示量化后的２５带子波系数,这一方案比单级零树方法节省约６％的码率,并且实现了空间和时间可尺主度化码流结构。     

ATM网中可变比特率小波视频编码和码率控制

本文提出了小波视频编码系统及其码率控制的新方法。对原始图像序列进行时间、水平和垂直三维小波分解,并采用改进的等级树集合分区的算法对小波系数进行量化。在图像组级上对该编码器进行码率控制,使其输出码流遵循漏桶控制器的参数,并对不同大小的漏桶缓冲器进行了讨论。仿真实验证明了该编码系统及其码率控制方法的有效性。     

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.     

A low memory zerotree coding for arbitrarily shaped objects

The set partitioning in hierarchical trees (SPIHT) algorithm is a computationally simple and efficient zerotree coding technique for image compression. However, the high working memory requirement is its main drawback for hardware realization. We present a low memory zerotree coder (LMZC), which requires much less working memory than SPIHT. The LMZC coding algorithm abandons the use of lists, defines a different tree structure, and merges the sorting pass and the refinement pass together. The main techniques of LMZC are the recursive programming and a top-bit scheme (TBS). In TBS, the top bits of transformed coefficients are used to store the coding status of coefficients instead of the lists used in SPIHT. In order to achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. A compact emplacement of the transformed coefficients is also proposed to further reduce working memory. The LMZC carefully treats "don't care" nodes in the wavelet tree and does not use bits to code such nodes. Comparison of LMZC with SPIHT shows that for coding a 768 /spl times/ 512 color image, LMZC saves at least 5.3 MBytes of memory but only increases a little execution time and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for some memory limited applications.