基于帧间去相关的超光谱图像压缩方法

针对超光谱图像的特点和硬件实现的实际需要,提出了一种基于小波变换的前向预测帧间去相关超光谱图像压缩算法。通过图像匹配和帧间去相关,消除超光谱图像帧间的冗余,对残差图像的压缩采用基于小波变换的快速位平面结合自适应算术编码的压缩算法,按照率失真准则控制输出码流,实现了对超光谱图像的高保真压缩。通过实验证明了该方案的有效性,基于小波变换的快速位平面结合自适应算术编码的压缩算法速度优于SPIHT,而且易于硬件实现。     

一种适于空间CCD图像压缩的SPIHT改进算法

针对传统遥感图像压缩多级树集合分裂排序(SP IHT)算法由于采用取决于图像内容的动态处理顺序而导致处理速 度缓慢这一问题,提出一种适于空间时间延时积分(TDI)CCD相机图像压缩的SPIHT改进算法 。首先,将小波变换后的图像分解成4×4块,同时对一个4×4块的 一个比特平面所有比特进行编码。为了实现并行处理机制,SPIHT改进算法重组传统SPIHT算 法的3个通道,然后采用并行和流水线作业的方式编码3个重组通道。实验结果表明,本文提 出的压缩算法可以稳定正常的 工作,具有良好压缩性能,平均信噪比(PSNR)性能与传统 方法相当,而数据吞吐率远高于传统方法。在正常100MHz时,数据 吞吐 率达到120Mpixle,大大提高压缩算法的处理速度。SPIHT改进算法 非常适合空间CCD相机图像压缩应用。     

改进SPIHT算法对医学图像的压缩

医学图像是医学诊断和疾病治疗的重要根据。为了实现图像的存储和远程医疗中快速传输图像的要求,必须对图像进行压缩。先分析CT医学图像经过小波变换后系数的统计特性,在基于小波变换的基础上,用SPIHT算法对CT医学图像进行压缩编码。提出了2种针对CT医学图像压缩编码改进的SPIHT算法。一是细化扫描产生的有效值,二是将小波变换后系数的低频近似部分按二进比特进行传输。用Matlab进行仿真,仿真结果表明改善了峰值信噪比。     

基于结合小波和脊波的分层图像压缩算法

基于分层思想,结合小波变换和脊波变换各自的优势,提出了一种新型的分层图像编码算法。首先使用一种各向异性扩散平滑方法,原始图像I经过平滑,得到平滑图像R1,然后用原始图像I减去平滑图像R1得到纹理图像R2。对平滑图像R1采用多级树集合分裂算法(Set partitioning in hierarchical trees,SPIHT)编码;对纹理图像R2进行改进的正交有限脊波变换,然后采用相应改进的SPIHT算法编码。实验结果表明,本算法在同等码率的情况下优于SPIHT算法的压缩性能。对于纹理比较丰富的图像,效果尤为明显。     

基于小波变换的图像快速压缩算法

结合目前广泛采用的嵌入式小波零树编码(EZW)和分层树集划分算法(SPIHT),提出了一种适用于图像小波变换高频压缩的提升算法.在经小波变换之后的矩阵中引入区间变化的概念,选取合适的数值取代区间中的数值,之后进行编码和传输.对于小波变换之后的图像在低频部分采用了DPCM算法,在高频部分采用了在EZW和SPIHT基础上改进的快速压缩算法,那么在编码时就可以用较短的时间保留原始图像的大部分能量,这对大幅图像的压缩和传输非常有利.虽然新算法与原先两种算法相比略微损失了部分保真度,但却在很大层度上降低了计算复杂度,缩短了编码时间.实验结果表明,此算法取得了较好的效果.     

基于小波图像编码算法的改进及实现

针对现有SPIHT算法存在的不足,提出了一种新的改进编码方案。该算法通过引入图像平滑预处理和边缘检测,很好地提高了图像视觉效果和重构图像的PSNR值,并且进一步改进了编码传输策略,对最低频子带单独进行编码,对高频子带采用无链表SPIHT算法。实验结果表明:该算法有效地克服了SPIHT算法存在的不足,其编码速度和图像恢复质量,均优于SPIHT算法(特别是对纹理丰富的图像)。     

基于背景位平面向低位位移的ROI压缩算法研究

针对JPEG2000中一般位移法消耗大量的比特数编码形状信息和最大位移法不能控制感兴趣区域(ROI)与背景(BG)区域相对质量的问题,以及经典多级树集合分裂(SPIHT)压缩算法中忽略小波子带兄弟间相关性的缺点,提出了一种应用于ROI压缩编码的BG位平面向低位位移的移位方法,并对SPIHT压缩编码算法的零树结构进行了改进,在解码端即使BG部分未被反向平移,仍能有较好的结果。通过缩小BG幅值及改进零树结构的SPIHT压缩算法的仿真实验表明,该方法实用有效,适用于较大压缩比及低码率传输的情况。     

多极化SAR图像3D-SPIHT压缩

该文针对多极化合成孔径雷达(SAR)图像在极化通道之间的相关性,提出了3D-SPIHT压缩方法。将多极化SAR图像(HH,HV,VV图像)作为一个整体,进行三维矩阵变换。首先在极化通道之间进行一维DCT变换,极化平面内进行二维离散小波变换(DWT),然后对3个极化混合系数平面采用分级树的集合划分(SPIHT)算法进行嵌入式统一混合编码。由于不是单独处理每一极化图像,因此不仅可以去除各极化图像内部之间的相关性,也可以去除极化通道之间的相关性。另外,由于采用统一嵌入编码,码流具有完全嵌入性,可以实现3个极化平面之间比特的精确自动分配。理论推导和仿真结果表明该方法对多极化SAR图像压缩是十分有效的。     

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.     

Real-time implementation of a new low-memory SPIHT image coding algorithm using DSP chip

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman's (1996) set partitioning in hierarchical trees (SPIHT) algorithm is well-known for its simplicity and efficiency. This paper deals with the real-time implementation of SPIHT algorithm using DSP chip. In order to facilitate the implementation and improve the codec's performance, some relative issues are thoroughly discussed, such as the optimization of program structure to speed up the wavelet decomposition. SPIHT's high memory requirement is a major drawback for hardware implementation. In this paper, we modify the original SPIHT algorithm by presenting two new concepts-number of error bits and absolute zerotree. Consequently, the memory cost is significantly reduced. We also introduce a new method to control the coding process by number of error bits. Our experimental results show that the implementation meets common requirement of real-time video coding and is proven to be a practical and efficient DSP solution.     

Compression of multispectral images by three-dimensional SPIHTalgorithm

The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3D) transform is taken (wavelet in the spatial domain, Karhunen-Loeve in the spectral domain) and a simple 3D SPIHT is used; in the second, after taking a spatial wavelet transform, spectral vectors of pixels are vector quantized and a gain-driven SPIHT is used. Numerous experiments on two sample multispectral images show very good performance for both algorithms     

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.     

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.     

MODIS多光谱图像压缩研究

为有效存储MODIS多光谱图像数据,该文提出一种基于谱间预测和整数小波变换的多光谱图像压缩算法.首先通过构造谱间最优预测器去除谱间冗余,再利用整数小波变换和SPIHT算法对预测误差图像去除空间冗余,最后进行自适应算术编码.该方法可实现MODIS多光谱图像的无损、近无损和有损压缩,取得了满意的实验结果;在不同小波基条件下与3D-SPIHT算法比较,表明了该方法的有效性.     

A compression algorithm for ECG based on integer lifting scheme wavelet transform

In view of the shortcomes of conventional ElectroCardioGram (ECG) compression algo- rithms,such as high complexity of operation and distortion of reconstructed signal,a new ECG compression encoding algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) is brought out after studying the integer lifting scheme wavelet transform in detail.The proposed algorithm modifies zero-tree structure of SPIHT,establishes single dimensional wavelet coefficient tree of ECG signals and enhances the efficiency of SPIHT-encoding by distributing bits rationally,improving zero-tree set and ameliorating classifying method.For this improved algorithm,floating-point com- putation and storage are left out of consideration and it is easy to be implemented by hardware and software.Experimental results prove that the new algorithm has admirable features of low complexity, high speed and good performance in signal reconstruction.High compression ratio is obtained with high signal fidelity as well.     

SAR image data compression using a tree-structured wavelettransform

SAR image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. The authors propose a compression scheme driven by texture analysis, homogeneity mapping and speckle noise reduction within the wavelet framework. The image compressibility and interpretability are improved by incorporating speckle reduction into the compression scheme. The authors begin with the classical set partitioning in hierarchical trees (SPIHT) wavelet compression scheme, and modify it to control the amount of speckle reduction, applying different encoding schemes to homogeneous and nonhomogeneous areas of the scene. The results compare favorably with the conventional SPIHT wavelet and the JPEG compression methods     

基于3维SPIHT编码的超光谱图像压缩

提出一种针对超光谱图像压缩的3维SPIHT编码算法.通过对超光谱图像进行3维小波变换,同时去除像素数据间的空间冗余和谱间冗余.针对变换后得到的小波系数,构造一种3维空间方向树结构,并用经3维扩展后的SPIHT算法(3D SPIHT算法)对小波系数进行量化编码.实验证明,基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时,表现出了优良的率失真性能.并且算法复杂度适中,具有嵌入式特性.     

A fast and efficient hybrid fractal-wavelet image coder.

The excellent visual quality and compression rate of fractal image coding have limited applications due to exhaustive inherent encoding time. This paper presents a new fast and efficient image coder that applies the speed of the wavelet transform to the image quality of the fractal compression. Fast fractal encoding using Fisher's domain classification is applied to the lowpass subband of wavelet transformed image and a modified set partitioning in hierarchical trees (SPIHT) coding, on the remaining coefficients. Furthermore, image details and wavelet progressive transmission characteristics are maintained, no blocking effects from fractal techniques are introduced, and the encoding fidelity problem common in fractal-wavelet hybrid coders is solved. The proposed scheme promotes an average of 94% reduction in encoding-decoding time comparing to the pure accelerated Fractal coding results. The simulations also compare the results to the SPIHT wavelet coding. In both cases, the new scheme improves the subjective quality of pictures for high-medium-low bitrates.     

Wavelet tree classification and hybrid coding for image compression

A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image.