小波树结构快速矢量量化编码方法

提出了基于人眼视觉属性和应用小波树结构２快速图象编码的矢量量化图象编码方法，简称为树结构快速矢量量化编码。在分析此方法矢量量化特点之后，设计产生码本的统计方法，并提出了矢量量化编码的快速算法。     

用于矢量量化的神经网络竞争学习算法

对典型的竞争学习算法进行了研究和分析，提出了一种基于神经元获胜概率的概率敏感竞争虎法。与传统竞争学习算法只有一个神经元获胜而得到学习不同，ＰＳＣＬ算法按照各种凶的获胜概率并通过对失真距离的调整使每个神经元均得到不同的学习，可以有效地克服神经元欠利用问题。     

基于小波变换的三维医学图象压缩技术的研究

满足诊断要求的医学影像无失真压缩编码技术的研究是图象压缩编码研究的新领域,也是ＰＡＣＳ系统和远程医疗诊断系统研究的热点之一。该文正是以ＣＴ,ＭＲＩ等医疗成像设备在临床应用时一次检查产生的人体某一部位的多帧断层图象组为对象,取目前编码领域最具潜力的小波变换技术,并将小波变换由二维推广至三维,同时将二维工邓列图象组视为三维图象而开展的医学影像编码技术研究。实验结果表明,在经临床医师确认的失真门限附近,     

基于自适应稀疏变换的指纹图像压缩

随着指纹识别技术的广泛应用,大量指纹图像需要被收集和存储.在指纹识别系统中,对于大容量的指纹数据库,指纹图像必须经过压缩后存储以减少存储空间,本文提出了基于自适应稀疏变换的指纹图像压缩算法.该算法在离线状态下提取指纹图像特征训练超完备字典;在编码过程中,首先利用差分预测编码和稀疏变换将待压缩指纹图像转换到稀疏域,然后对直流系数和稀疏表达系数进行量化和熵编码,从而实现图像信息的压缩.实验表明,在中低码率段,本文算法相比于JPEG、JPEG2000和WSQ等主流压缩算法表现出更优越的率失真性能;在相同码率时,本文算法生成的压缩图像的主观视觉效果更好,指纹识别率更高.     

Multiresolution HVS and statistically based image coding scheme

In this paper a novel multiresolution human visual system and statistically based image coding scheme is presented. It decorrelates the input image into a number of subbands using a lifting based wavelet transform. The codec employs a novel statistical encoding algorithm to code the coefficients in the detail subbands. Perceptual weights are applied to regulate the threshold value of each detail subband that is required in the statistical encoding process. The baseband coefficients are losslessly coded. An extension of the codec to the progressive transmission of images is also developed. To evaluate the performance of the coding scheme, it was applied to a number of test images and its performance with and without perceptual weights is evaluated. The results indicate significant improvement in both subjective and objective quality of the reconstructed images when perceptual weights are employed. The performance of the proposed technique was also compared to JPEG and JPEG2000. The results show that the proposed coding scheme outperforms both coding standards at low compression ratios, while offering satisfactory performance at higher compression ratios.     

基于ROI编码的任意尺寸测量图像的压缩方法

针对基于感兴趣区域(ROI,Region of Interest)的测量图像算法中存在的编码冗余、适用范围受限等问题,提出了一种适用于任意分辨率的测量图像压缩算法。该方法通过加入ROI中A类集合的分类,优化了码流的输出,进一步提高了算法的编码效率;根据小波变换的特点和测量图像对编码算法的要求,通过分区域编码解决了测量图像在编码过程中分辨率受限的问题。实验表明,方法实现了对任意分辨率测量图像的编码,并在不影响测量精度的前提下,获得了优于基于感兴趣区域的测量图像压缩算法的压缩性能。     

An efficient encryption-then-compression technique for encrypted images using SVD

This paper presents a lossy compression technique for encrypted images using Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD) and Huffman coding. The core idea of the proposed technique lies in the selection of significant and less significant coefficients in the wavelet domain. Significant and less significant coefficients are encrypted using pseudo-random number sequence and coefficient permutation respectively. Furthermore, encrypted significant data is compressed by quantization and entropy coding while, less significant encrypted data is efficiently compressed by discarding irrelevant information using SVD and Huffman coding techniques. At receiver side, a reliable decompression and decryption technique is used to reconstruct the original image content with the help of compressed bit streams and secret keys. The performance of proposed technique is evaluated using parameters such as Compression Ratio (CR) and Peak-Signal-to-Noise Ratio (PSNR). Experimental results demonstrate the effectiveness of proposed work over prior work on compression of encrypted images and obtain the compression performance comparable to state of art work on compression of unencrypted images i.e. JPEG standard.     

基于字典学习的卫星图像压缩算法研究

针对卫星图像的特点及当前卫星图像在传输和存储上面临的问题,提出了一种基于稀疏表示的卫星图像二级无损压缩算法。通过传输稀疏表示后的稀疏系数来代替图像本身的传输,完成对卫星图像的第一级压缩;对非零稀疏系数先作预处理后实现聚类,然后依据聚类索引对原始非零稀疏系数的位置排序;最后对处理后的非零稀疏系数和位置数据分块,并利用改进的自适应哈夫曼算法对非零稀疏系数的数据块编码,利用差分编码和改进的自适应哈夫曼算法对位置数据块编码,完成对图像数据的第二级压缩。实验结果表明,与传统算法相比,所提算法具有明显优势,改进算法的压缩率是传统算法的1/3~1/2,且可同时实现卫星图像的高倍无损压缩与高分辨率重建。     

HEVC-based lossless intra coding for efficient still image compression

Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is the compression of high resolution video, its performance in still image compression is at par with state-of-the-art still image compression standards. This work explores the possibility of incorporating the efficient intra prediction techniques employed in HEVC into the compression of high resolution still images. In the lossless coding mode of HEVC, sample- based angular intra prediction (SAP) methods have shown better prediction accuracy compared to the conventional block-based prediction (BP). In this paper, we propose an improved sample-based angular intra prediction (ISAP), which enhances the accuracy of the highly crucial intra prediction within HEVC. The experimental results show that ISAP in lossless compression of still images outclasses archival tools, state-of-the-art image compression standards and other HEVC-based lossless image compression codecs.     

Textual image compression at low bit rates based on region-of-interest coding

In this paper, we deal with those applications of textual image compression where high compression ratio and maintaining or improving the visual quality and readability of the compressed images are of main concern. In textual images, most of the information exists in the edge regions; therefore, the compression problem can be studied in the framework of region-of-interest (ROI) coding. In this paper, the Set Partitioning in Hierarchical Trees (SPIHT) coder is used in the framework of ROI coding along with some image enhancement techniques in order to remove the leakage effect which occurs in the wavelet-based low-bit-rate compression. We evaluated the compression performance of the proposed method with respect to some qualitative and quantitative measures. The qualitative measures include the averaged mean opinion scores (MOS) curve along with demonstrating some outputs in different conditions. The quantitative measures include two proposed modified PSNR measures and the conventional one. Comparing the results of the proposed method with those of three conventional approaches, DjVu, JPEG2000, and SPIHT coding, showed that the proposed compression method considerably outperformed the others especially from the qualitative aspect. The proposed method improved the MOS by 20 and 30 %, in average, for high- and low-contrast textual images, respectively. In terms of the modified and conventional PSNR measures, the proposed method outperformed DjVu and JPEG2000 up to 0.4 dB for high-contrast textual images at low bit rates. In addition, compressing the high contrast images using the proposed ROI technique, compared to without using this technique, improved the average textual PSNR measure up to 0.5 dB, at low bit rates.     

基于小波包分解的 SAR图像压缩 *

针对 SAR图像含有丰富的中、高频信息 ,而基于小波变换的图像压缩方法会丢失高频细节信息 ,提出了基于小波包分解的 SAR图像编码算法。小波包变换对 SAR图像进行完全分解 ,再用与后续编码器相关联的代价函数进行最佳基搜索 ,然后根据各子带小波包系数的重要性进行加权 ,采用多级树集合分裂算法 ( SPIHT)编码。实验结果表明 ,该算法更好地保留了 SAR图像的细节信息 ,获得了同压缩比下优于传统 SPIHT算法的编码性能 ,更有利于后续图像处理。     

Image Data Compression using Zonal Sampling and Piecewise-Linear Transforms

In this paper, image data compression methods based on sample selection in the piecewise-linear transform domain will be presented. The image is subjected to a 2-dimensional piecewise-linear transformation and some coefficients will be selected using threshold method and a proposed zonal sampling method. In the proposed zonal sampling method all samples outside the selected zone will be discarded completely (no zeros are replaced). The inverse transformation in this case will have a dimension equal to that of the selected zone. Thus the number of computations needed for the inverse transformation is reduced. The Peak Signal-to-Noise Ratio (PSNR) is used as a measure of quality of the reconstructed images. Comparisons of the compression ability using piecewise-linear transforms and some selected orthogonal transforms such as Walsh and cosine transforms are given. The results obtained by using the proposed zonal sampling method show that the piecewise-linear transforms have a better performance than the orthogonal transforms.     

Image subband coding using fuzzy inference and adaptive quantization

Wavelet image decomposition generates a hierarchical data structure to represent an image. Recently, a new class of image compression algorithms has been developed for exploiting dependencies between the hierarchical wavelet coefficients using zerotrees. This paper deals with a fuzzy inference filter for image entropy coding by choosing significant coefficients and zerotree roots in the higher frequency wavelet subbands. Moreover, an adaptive quantization is proposed to improve the coding performance. Evaluating with the standard images, the proposed approaches are comparable or superior to most state-of-the-art coders. Based on the fuzzy energy judgment, the proposed approaches can achieve an excellent performance on the combination applications of image compression and watermarking.     

多视点视差估计中基于图像块特征矢量和相似尺度的参考视点选择方法

在多视点视频处理中，采用多参考视点的视差估计方法能够在很大程度上消除各个视点间的冗余性，达到对多视点视频数据有效编码压缩的目的。研究了多视点信号处理中视差估计的多参考视点选择问题，采用快速Walsh变换谱构造图像块特征矢量，并提出了基于图像块特征和相似尺度的多参考视点选择方法。实验结果证明，本文方法能在保证编码质量的前提下，减少视差估计的计算复杂度。     

基于快速对称小波变换的高效模糊指纹图象压缩方法

自动指纹识别系统ＡＦＩＳ（ａｕｔｏｍａｔｅｄｆｉｎｇｅｒｐｒｅｎｔｉｄｅｎｔｉｆｉｃａｔｉｏｎｓｙｓｔｅｍ）要求对支持该系统的数据库中的指纹图象数据进行压缩，以提高系统实用性，其恢复图象要求有很高的保真度，以避免丢失细节．同时，在实用系统中，还需要有较快的图象恢复时间．本文总结一种基于对称小波变换的高效模糊指纹图象压缩方法，通过对称快速小波变换，选择适当的分解方式，采用自适应标量量化，加上改进的摘编码，实现高速、高保真的要求．同时，设计一种码率控制策略，有效地控制压缩量．实验结果表明：本文的压缩方法能表现出很好的性能．     

基于HEVC与聚类算法的计算机桌面图像压缩算法

对基于HEVC与聚类算法的计算机桌面图像压缩方案进行研究,将计算机桌面图像进行自适应块分类,利用颜色聚类算法对块进行颜色聚类,简化颜色总类、节省码流、改善编码性能。再利用HEVC帧内预测编码算法对块进行压缩,为高清分辨率计算机桌面图像网络共享,提供高效的图像压缩方法。     

SAR复图像数据的CCSDS-IDC编码性能分析与四叉树编码

目的：CCSDS-IDC (国际空间数据系统咨询委员会-图像数据压缩) 是NASA制定的基于离散小波变换(DWT)尺度间衰减性的空间图像数据压缩标准,适用于合成孔径雷达(SAR)幅度图像及各类遥感图像的压缩。然而,与光学图像不同,常见的SAR图像都是复图像数据,其在干涉测高等许多场合具有广泛应用,分析研究CCSDS-IDC对SAR复图像数据的编码性能具有重要的应用价值。方法：由于SAR复图像数据不具有尺度间的衰减性,因此将其用于SAR复图像数据编码时性能较低。考虑到SAR复图像数据DWT系数呈现出聚类特性,提出将四叉树(QC)用于DWT域的SAR复图像数据编码,发现QC对SAR复图像数据具有高效的压缩性能。结果：实验结果表明,在同等码率下,对基于DWT的SAR复图像数据压缩, QC比CCSDS-IDC最多可提高幅度峰值信噪比4.4dB,平均相位误差最多可降低0.368;与基于方向提升小波变换（DLWT）的CCSDS-IDC相比,QC可提高峰值信噪比3.08dB,降低平均相位误差0.25;对其它类型的图像压缩,基于聚类的QC仍能获得很好的编码性能。结论：CCSDS-IDC对SAR复图像数据编码性能低下,而QC能获得很好的编码性能。对应于图像平滑分布的尺度间衰减性,其在某些特殊图像中可能不存在,而对应于图像结构分布的聚类特性总是存在的,故在基于DWT的图像编码算法设计中,应优先考虑利用小波系数的聚类特性,从而实现对更多种类图像的高效编码。     

基于SPIHT和分类比特编码的无损图像压缩

在整数小波变换的基础上提出了一种基于SPIHT分类比特平面上下文编码的无损图像压缩算法,和其他方法比较,把变换后的系数分成符号模板、高位比特位、低位比特位三类,对高位比特位的系数用SPHIT算法进行编码,而符号模板和低位比特位用基于上下文的算术编码,在低位比特位的上下文编码中不仅考虑了同一位平面的相邻位置相关的上下文,也考虑位平面之间的上下文。实验表明,提出的方法具有较好的压缩效果。     

基于分类KLT的高光谱图像压缩

高光谱图像的有效压缩已经成为高光谱遥感领域研究的热点。提出了一种基于分类KLT（ Karhunen-Loeve Transform）的高光谱图像压缩算法。该算法利用光谱信息对高光谱图像进行地物分类,根据相邻波段的相关性对高光谱图像进行波段分组。在地物分类与波段分组的基础上,对每组的每一类地物数据分别进行KL变换,利用EBCOT（Embedded Block Coding with Optimal Trtmcation）算法对所有主成分进行联合编码。实验结果表明,该算法能够取得优于JPEG2000以及DWT-JPEG2000的压缩性能,适合实现高光谱图像的有效压缩。     

一种基于形态小波的遥感影像压缩编码算法

与一般图像不同,由于遥感影像具有纹理复杂、局部相关性较弱的特点,而且影像经过小波变换后系数的空间聚类特性较明显,因此遥感影像压缩具有一定的特殊性,可是目前大多数基于小波的压缩编码算法都没有考虑小波系数的空间聚类特性,为了进一步提高编码效率,提出了一种基于形态小波的遥感影像压缩算法。该算法首先对遥感影像进行多尺度快速小波变换,然后依据遥感影像的小波能量聚类特性,采用一种形态膨胀编码算法来实现遥感影像的高效压缩编码。试验结果表明,对一般遥感图像,该算法在高倍率压缩的情况下要优于目前的JPEG2000算法;而且对多波段的遥感影像,该算法也取得了较好的压缩效果。