基于谱间DPCM和整数小波变换的超光谱图像无损压缩

分析了干涉成像光谱仪所获取图像的谱间和空间相关性,提出了一种混合无损压缩方案.首先进行谱间DPCM预测,再对残差图像采用整数小波变换,最后对小波系数进行二值自适应算术编码.实验结果表明,该算法可实现无损压缩,压缩比平均可达2.018,较二维整数小波变换算法提高40.3%.并且算法复杂度较低,只有加减和移位运算,易于硬件实现.     

基于提升方案的多光谱遥感图像有损压缩算法

在分析多光谱遥感图像谱间和空间数据特点的基础上,提出了一种DPCM线性预测与基于提升方案的整数小波变换相结合的多光谱遥感图像有损压缩算法。在谱间采用DPCM预测去除谱间相关性;在谱内采用整数小波变换去除空间相关性,根据不同子带对目标识别的重要程度,选择不同的量化阈值和量化步长进行量化,并分别对各个子带量化后的数据和重要图表采用固定比特平面编码和游程编码,实现高效的多光谱遥感图像压缩。实验结果表明,该算法在一定的压缩比下,重构图像具有较高的峰值信噪比,并且算法硬件实现简单,对内存的需求低。     

基于网格编码量化的超光谱图像压缩

提出了一个基于小波网格编码量化的超光谱图像压缩方法。谱间和空间冗余处理构成了超光谱图像压缩算法的主要内容,该算法使用一个谱间差分预测步骤来去除谱间冗余,而后对预测残差图像进行小波变换并利用均匀阈值网格编码量化(trellis-coded quantization)方法来量化各小波子带,最后使用自适应算术编码对量化码字进行熵编码。为使编码器能为所有子带获取率-失真意义上最优的量化阈值,设计了一个基于子带统计特性和网格编码量化器率-失真特性的比特分配算法。在实验中,该算法表现出优良的压缩性能,对于实验的超光谱图像,该方法在压缩比为32时可得到37．1dB的峰值信噪比,这表明本算法能有效压缩超光谱图像,适于超光谱图像压缩应用。     

一种彩色图像的小波域预测编码方法

彩色图像存在空间相关性和帧间相关性,如何有效的去除这两种相关性是提高彩色图像压缩比的关键。提出一种小波变换域的自适应预测无损压缩方法,分为三个步骤:首先对彩色图像各个分量进行可逆整数小波变换减小空间相关性,然后利用联想记忆神经网络对小波系数图像分别进行空间预测和帧间预测,进一步去除空间和帧间相关性,最后对预测残差进行算术编码。实验证明该算法是可行的和有效的,对测试图像的压缩效果优于JPEG LS标准。     

基于1+2维小波变换的多光谱图像压缩

提出利用1+2维小波变换技术对多光谱遥感图像进行近无损压缩.使用嵌入式块截断算法压缩谱内图像;谱间变换使用短滤波器进行1维小波变换,去除多光谱图像的谱间冗余.实验证明,加入谱间的1维变换比不加入变换,而仅仅对每个波段图像进行压缩的压缩比提高20%.整个算法是嵌入式的,复杂度适中.编码的比特流具有渐进式特性,可以过渡为高压缩比的有损压缩.     

高光谱图像的分布式近无损压缩

星载高光谱图像的有效压缩已经成为高光谱遥感领域亟待解决的难题。分布式信源编码具有较低的编码复杂度与良好的抗误码性,在高光谱图像压缩领域具有广阔的应用前景。提出了一种基于多元陪集码的高光谱图像分布式近无损压缩算法。根据多元陪集码的Slepian-Wolf无损编码的压缩过程,提出了面向高光谱图像分布式近无损压缩的最优量化方案,使得高光谱图像在给定目标码率条件下的失真达到最小,在此基础上对量化值进行Slepian-Wolf无损编码,从而实现了高光谱图像的分布式近无损压缩。实验结果表明,与典型的传统算法相比,该算法取得了较好的近无损压缩性能和较低的编码复杂度。     

适于星上应用的高光谱图像无损压缩算法

针对常见基于预测、变换、矢量量化及其组合的高光谱无损压缩算法压缩比低、压缩算法整体耗时以及硬件实现困难的问题,提出一种适于星上应用的高光谱图像无损压缩算法。首先,沿光谱线的第一谱段图像采用中值预测器进行谱段内预测,其他谱段图像采用谱间预测。谱间预测采用两步双向预测算法,第一步预测采用双向二阶预测器得到参考预测值,第二步预测采用本文提出的改进LUT搜索预测算法得出4个LUT预测值,然后将参考预测值与其比较得出最终的预测值。最后,使用XX-X空间高光谱相机的压缩系统试验设备对该文提出的压缩算法进行了试验验证。结果表明,压缩系统能快速稳定地工作,平均压缩比达到3.05 bpp,与传统方法相比,平均压缩比提高了0.14~2.94 bpp。有效的提高了高光谱图像无损压缩比和解决了压缩算法整体实现困难的问题。     

干涉光谱图像序列压缩的实验研究

基于均匀值量化压缩算法和AR模型算法,对干涉型超光谱成像仪直接产生的实际干涉图像序列进行了近无损压缩仿真实验.AR模型算法对残差采用Lloyd-Max量化器进行量化,对量化输出结果用Huffman编码器进行编码.分析了AR模型阶数对压缩性能的影响.结果指出,均匀值量化和AR模型算法均能满足干涉光谱图像序列压缩的要求.     

有序抖动半调图像的无损压缩算法

在分析不同有序抖动模板对所生成抖动半调图像的影响因素基础上,设计了尺度变化的分块及块间异或预处理策略,大大提高了二值游程长度.进而使用游程和哈夫曼相结合的编码思想提出了一种新的有序抖动半调图像无损压缩算法.仿真实验表明,该算法在编码效率上具有较大优势.     

定点脉冲线性预测编码方法研究*

在多脉冲线性预测编码的基础上,本文提出了位置无关脉冲搜索算法。该算法不需要搜索脉冲位置,而是根据给定的脉冲位置一次性解出脉冲幅度矢量。这就保证了得到的脉冲组合在最小二乘意义下是最优的,为改进合成语音质量提供了理论基础。进而在激励脉冲与位置无关的理论基础上,提出了定点脉冲线性预测编码方法。对所提出的算法在MATLAB下进行了仿真,仿真结果发现位置无关脉冲搜索算法得到的合成语音质量优于序贯法,编码时间也要比序贯法短。定点脉冲线性预测编码方法可以在2.7 kbps的编码速率下获得与G.729相近的合成语音。     

凸面光栅成像光谱仪图像数据实时压缩技术

凸面光栅成像光谱仪图像数据量巨大,给数据的传输和存储带来了压力,因此要对图像数据进行实时压缩。首先根据成像原理对图像数据的特点进行了分析,得出去除空间相关性和谱间相关性的压缩途径;然后,进行了压缩算法分析,提出了在谱间进行一阶线性预测、谱内进行JPEG2000压缩的三维压缩方案;最后,设计了基于FPGA+ADV212的实时压缩系统,其中FPGA用于逻辑控制和预测算法实现,ADV212用于JPEG2000压缩。分析结果表明:该系统具备无损和有损压缩能力,能够实现图像数据实时压缩。     

Distributed Lossless Compression Algorithm for Hyperspectral Images Based on Classification

This article addresses the problem of distributed lossless compression for hyperspectral images and proposes an effective lossless compression algorithm based on classification. First, a band selection algorithm was performed on the hyperspectral images to select those bands with considerable information. Next, the K-means algorithm was performed on those selected bands to obtain the classification map. To make full use of the spectral and spatial correlation, a multilinear regression model was introduced to construct the high-quality side information of each class within the identical block according to the classification map. Subsequently, the (n, k) linear grouping codes were employed to perform the distributed source coding for each class separately. The experimental results showed that the proposed algorithm has a competitive lossless compression performance compared with other state-of-the-art algorithms.     

基于分类和曲线拟合的干涉超光谱图像压缩

根据干涉超光谱图像的特点,提出了一种基于图像分类与曲线拟合的干涉超光谱图像数据分解算法,结合内嵌比特平面编码技术实现干涉超光谱图像的压缩。与JPEG2000一样,该算法实现了有损、无损压缩的兼容。将干涉超光谱图像数据分为主干涉区域与非主干涉区域两类,针对主干涉区域提出了一种相似匹配算法,而对非主干涉区域采用经验模式分解和二次曲线拟合方法进行数据分析,两种分析算法结合起来能够有效地对谱线数据进行分解,从而有利于取得更好的压缩效果。仿真结果表明,提出的算法可以使无损压缩的输出码率降低0.2~0.4bit/pixel,而近无损、限失真压缩的重建图像质量相应提高。     

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are introduced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ),the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.     

Joint Lossless Image Compression and Encryption Scheme Based on CALIC and Hyperchaotic System

For efficiency and security of image transmission and storage, the joint image compression and encryption method that performs compression and encryption in a single step is a promising solution due to better security. Moreover, on some important occasions, it is necessary to save images in high quality by lossless compression. Thus, a joint lossless image compression and encryption scheme based on a context-based adaptive lossless image codec (CALIC) and hyperchaotic system is proposed to achieve lossless image encryption and compression simultaneously. Making use of the characteristics of CALIC, four encryption locations are designed to realize joint image compression and encryption: encryption for the predicted values of pixels based on gradient-adjusted prediction (GAP), encryption for the final prediction error, encryption for two lines of pixel values needed by prediction mode and encryption for the entropy coding file. Moreover, a new four-dimensional hyperchaotic system and plaintext-related encryption based on table lookup are all used to enhance the security. The security tests show information entropy, correlation and key sensitivity of the proposed methods reach 7.997, 0.01 and 0.4998, respectively. This indicates that the proposed methods have good security. Meanwhile, compared to original CALIC without security, the proposed methods increase the security and reduce the compression ratio by only 6.3%. The test results indicate that the proposed methods have high security and good lossless compression performance.