Image compression using VQ-BTC

Vector quantization (VQ) and block truncation coding (BTC) are successful image compression techniques. However, a reproduced image using VQ or BTC suffers from edge degradation. A new technique that combines the advantages of both VQ and BTC to combat this degradation is presented and is referred to as VQ-BTC. In VQ-BTC, a low-detail block is encoded using VQ. For a high-detail block, a modification of BTC is used to determine the locations of the relatively lighter and relatively darker pixels inside the block and VQ is then used to encode each. VQ-BTC provides improved edge reproduction and much lower bit rates than those obtained by BTC     

Decoding algorithm for fractal image compression

A new iterative decoding method is proposed for fractal image compression. Convergence properties are provided. Experimental results show the superiority of the new method over the conventional decoding procedure     

海洋合成孔径雷达图像的一种高效压缩方法

针对通用压缩算法未利用合成孔径雷达（SAR）图像特征的不足,提出一种基于概率分布的自适应海洋SAR图像压缩算法。利用海洋SAR图像的概率分布,根据目标的分布设计量化方案,使目标和背景得到不同程度的保留。利用场景的稀疏性,将阈值以上的像素映射到三元组,对其灰度和位置信息分别熵编码;利用剩余背景层灰度偏差较小的特点作位平面编码。实验结果表明,该算法能有效地压缩图像,同码率下峰值信噪比（PSNR）较JPEG2000高5dB～10dB。本文算法复杂度低,对比度保持好,适用于针对不同需求的海面舰船SAR图像压缩。     

高速采集图像实时压缩算法

为了解决高速图像采集给图像数据的实时传输和存储带来的困难,提出了一种基于提升形态Haar小波的面向自动目标识别(ATR)的图像压缩算法——目标形态小波图像编码(TMWC).该算法将形态Haar小波与数学形态学方法结合应用.首先将采集图像进行二维提升形态Haar小波分解,结合目标感兴趣区域(ROI)检测要求的特点,仅在尺...     

Listless zerotree image compression algorithm

Recent years ,wavelet transformhas been widely usedin i mage compression because of its outstanding ti me-frequency localization ability and de-correlation ability.Among all the i mage compression algorithms ,EZW[1]and SPIHT[2]are two of the most influent…     

Technique for fractal image compression using genetic algorithm

A new method for fractal image compression is proposed using genetic algorithm (GA) with an elitist model. The self transformation property of images is assumed and exploited in the fractal image compression technique. The technique described utilizes the GA, which greatly decreases the search space for finding the self similarities in the given image. This article presents theory, implementation, and an analytical study of the proposed method along with a simple classification scheme. A comparison with other fractal-based image compression methods is also reported.     

Adaptive lossy LZW algorithm for palettised image compression

An adaptive lossy LZW algorithm is proposed for palettised image compression, that is a generalised algorithm of the conventional lossless LZW algorithm. The new algorithm employs an adaptive thresholding mechanism with human visual characteristics to constrain the distortion. With this distortion control, the compression efficiency is increased by ~40% for natural colour images, while maintaining good subjective quality on the reconstructed image. In addition, the encoded image file format is compatible with the original GIF decoder     

一种改进的分形图像压缩算法

为了缩短分形编码时间,通过对图像定义域块和值域块的统计特性分析,提出了改进的分形压缩算法。设计最优匹配定义域块自适应搜索方法,缩短搜索范围;用值域块均值代替灰度偏移量,减少计算量。实验证明,运用这种改进的分形图像压缩算法进行图像压缩,在保持较高的解压图像质量的情况下,大幅缩短图像压缩编码时间。     

一种新的图像压缩编码算法

对图像压缩编码算法进行了改进。首先,将小波分解后的3个高频系数进行预处理:将高频部分进行球坐标变换,降低了同一尺度内系数的相关性;基于小波域和球坐标域的两个前提,定义了多尺度模积的概念,用来控制收缩函数对小波高频部分进行收缩处理。这样,可以去除那些不影响视觉效果的小波系数以及噪声信息,达到较高的压缩比。然后,对小波变换的低频部分进行单独编码(DPCM),对球坐标下的高频部分采用改进的多级树集合分裂(SPIHT)编码。针对SPIHT编码中重复扫描的问题,引入了最大值矩阵MMP(matrix of maximum pixel),这种策略能够有效降低比较次数。仿真实验表明,本文提出的算法具有较好的编码效率。     

基于上下文选择编码的小波图像编码算法

提出了基于上下文选择编码的小波图像编码算法:在独立同分布情况下,比较了直接对重要性信息进行算术编码、定长游程编码以及变长游程编码的理想平均比特损耗;分析了由于估计概率误差以及概率时变的存在从而导致了实际平均比特损耗增大;提出了新的上下文模型;根据上下文选择使用算术编码、定长游程编码或者变长游程编码.实验结果表明,在0.25bit/pixel下,新算法较算术编码方案编码时间节省了20%以上;与EBCOT相比,新算法提高了0～0.28dB;尤其适合于低码率应用.     

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。     

基于灰度值分割的感兴趣区域图像压缩算法

基于JPEG2000中感兴趣区域(ROI)图像压缩标准,提出了一种新型ROI图像压缩算法。算法根据用户指定的ROI条件,确定灰度值的重要位平面,然后将灰度值分割为重要灰度值和次要灰度值,优先对重要灰度值进行编码。根据比特率要求对次要灰度值进一步编码。实验结果表明:提出的算法同时具有最大平移法和一般平移法的优点,不仅能够灵活控制ROI和背景区域图像质量在码率上的分配,还能支持任意形状的ROI编码而不需要编码形状信息。另外,由于在小波变换前就进行了灰度值分割,所以在低码率时能够有效提高计算速度并节省存储空间。     

一种基于LBT的分布式图像压缩算法

无线多媒体传感器网络(WMSNs)中传感器节点采集的数据量非常大,在传输前需对大数据量的多媒体信息进行压缩处理,但是单节点能源受限,存储、处理能力相对较弱。针对无线多媒体传感器网络应用的高效、低耗能的需求这些问题,在图像压缩双正交重叠变换(LBT)的基础上,文中提出了一种基于此变换的分布式无线多媒体传感器网络图像压缩算法。即基于簇结构,把压缩任务分配给其他节点,通过多个节点相互协作,共同完成图像的压缩编码和传输。实验结果表明,在传感器节点散布不均且较为密集的情况下,该算法在高质量、低复杂度和低功耗等方面都有了很大的性能提高。     

改进的单幅红外图像局部自适应非均匀校正

为了实现对单幅红外图像的非均匀性校正并且对局部细节的校正效果进行优化,提出了一种局部自适应的非均匀校正算法。算法利用红外焦平面阵列的固定图像噪声呈单方向分布的特性,采用基于高斯权重思想的中值直方图非均匀算法实现红外图像的单参数校正;然后将图像分块,使图像的各局部都能够自适应地选择各自最合适的校正参数,达到优化细节的校正效果。实验结果及分析表明:与单参数中值直方图非均匀校正算法相比,提出的算法在均方根误差、峰值信噪比、图像平滑性等方面都得到了进一步的改善,并且保留了更多的图像细节,为非均匀校正提供了一种方法。     

Genetic algorithm based on discrete wavelet transformation for fractal image compression

In this paper, a genetic algorithm (GA) based on discrete wavelet transformation (DWT) is proposed to overcome the drawback of the time-consuming for the fractal encoder. First, for each range block, two wavelet coefficients are used to find the fittest Dihedral block of the domain block. The similar match is done only with the fittest block to save seven eighths redundant MSE computations. Second, embedding the DWT into the GA, a GA based on DWT is built to fast evolutionary speed further and maintain good retrieved quality. Experiments show that, under the same number of MSE computations, the PSNR of the proposed GA method is reduced 0.29 to 0.47 dB in comparison with the SGA method. Moreover, at the encoding time, the proposed GA method is 100 times faster than the full search method, while the penalty of retrieved image quality is relatively acceptable.     

一种适于高光谱图像压缩的相关系数矩阵近似计算算法

为充分利用高光谱图像的强谱间相关性,许多学者提出了基于相关系数矩阵的波段预处理的图像压缩算法,得到很好的压缩性能。但是由于相关系数矩阵计算量过大,严重制约了波段预处理方法的实时性。文中提出一种相关系数矩阵近似计算算法,通过对高光谱图像进行空间域采样,以很小的计算量求得近似的相关系数矩阵。用真实高光谱图像数据的实验结果表明,该算法在几乎不影响压缩性能的前提下,能大幅降低计算量,使得波段预处理方法的实时实现成为可能。     

A lossless compression algorithm of remote sensing image for space applications

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.     

A modified-set partitioning in hierarchical trees algorithm for real-time image compression

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman’s set partitioning in hierarchical trees (SPIHT) algorithm is well known for its simplicity and efficiency. SPIHT’s high memory requirement is a major drawback to hardware implementation. In this study, we present a modification of SPIHT named modified SPIHT (MSPIHT), which requires less execution time at a low bit rate and less working memory than SPIHT. The MSPIHT coding algorithm is modified with the use of one list to store the coordinates of wavelet coefficients instead of three lists of SPIHT; defines two terms, number of error bits and absolute zerotree; and merges the sorting pass and the refinement pass together as one scan pass. Comparison of MSPIHT with SPIHT on different test image shows that MSPIHT reduces execution time at most 7 times for coding a 512 × 512 grayscale image; reduces execution time at most 11 times at a low bit rate; saves at least 0.5625 MB of memory; and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for real-time and memory limited mobile communications. Published in Russian in Radiotekhnika i Elektronika, 2008, Vol. 53, No. 6 pp. 676–685. The text was submitted by the authors in English.     

视觉传感器网络中基于散度模型的协作式图像压缩机制

结合视觉传感器网络的协同工作特性,提出一种基于散度模型的图像压缩机制.理论分析和实验结果表明,该压缩机制不仅可以减少图像数据量,而且由于压缩后每字节数据所含信息量由各簇内节点的二值量化像素均分,不会引起传输错误在图像中大面积扩散.相比于采用传统的图像压缩算法,随着平均分组丢失率的增高,接收图像峰值信噪比较高.