基于位平面可预测性的无损图像压缩研究

提出了一种基于嵌入式位平面的静止连续色调图像的无损图像压缩方法：通过将1幅图像分割成两类位平面（基础层和增强层）使得该图像具有了位平面的可测量性，并且通过利用平面与平面以及每个平面中各像素之间的相关性减少冗余，从而获得优秀的压缩性能；与其他压缩算法的比较表明，基于嵌入式位平面的无损图像压缩算法由于具有位平面可测量性而体现了巨大的优越性。     

基于码率预分配编码技术的高分辨率航测图像压缩

由于需要对大面阵航空CCD相机带来的庞大航测图像数据进行压缩,在研究多种图像压缩算法的基础上提出了一种基于比特位平面编码的码率预分配图像压缩算法(RPCA)。首先将图像进行多级整数小波变换,以去除图像像素之间相关冗余。根据率失真理论并结合各个子带对图像重建质量的重要性原则,编码前事先确定每个子带在总码率一定的情况下各个子带在实际编码中应当分配的码率大小,再利用自适应MQ算术编码对每个子带比特平面进行熵编码,从而得到细致的嵌入式码流。实验仿真结果表明,该RPCA码率分配精准,图像压缩质量与JPEG2000标准相当,且支持无损到有损的任意倍率图像压缩,但复杂度低于JPEG2000标准,适合于硬件的高速实现。     

基于改进图像块分类算法的图像压缩方法

本文提出了一种基于BP神经网络和改进的图像块分类算法的有效图像压缩方法.首先采用改进的图像块分类算法将图像块划分为互不重叠的3大类图像块,即平滑块、目标块、边缘块;然后基于BP神经网络对平滑块和目标块选用合适的隐含层单元数量进行压缩,对边缘块则采取不压缩而直接保存到压缩数据的方法,最后,得到上述3类图像块压缩数据集的集合.相比于对3类图像块同时进行压缩,该方法相对传统的图像压缩方法节省了0.469 s、峰值信噪比(PSNR)提高了2.11 dB,并使压缩率提高了5.25%,能够更加有效地经过图像压缩后保持细节信息.     

基于小波分析的BP网络含噪图像压缩算法的探讨

近年来BP神经网络在图像压缩技术中的应用已成为图像压缩的热点问题之一。与经典的压缩算法相比,具有更高的压缩比和重构图像质量。但实际上,原始图像经常受到噪声的污染,使得图像质量明显下降。通过研究小波图像去噪的方法,结合小波变换和BP神经网络,研究了基于小波域的BP神经网络图像压缩方法。先在小波域内对图像进行去噪,再用BP神经网络进行压缩,从而得到高质量的重构图像。结果表明,该算法对含噪声图像的压缩,获得了良好的图像质量,同时也证明了这种方法的有效性。     

基于频率分级的指纹图像压缩算法

指纹图像是由黑白相间的脊线、谷线排列在一起而构成的特殊灰度图像,反复出现的反差边缘、周围的背景区域使得指纹图像具备低、中、高三种不同的频率成分,本文利用小波包变换和频谱分析提出了一种基于频率分级的指纹图像压缩算法,对包含能量最多的低频子图像,采用无损差分脉冲编码(DPCM)算法;对包含能量较少的中频子图像,采用嵌入式小波零数编码(EZW)算法;对包含能量最少的高频子图像,采用集合分裂嵌入块编码(SPECK)算法。仿真实验表明,本算法在保证重建质量的前提下,压缩比平均提高了21.4%左右,逼真度平均提高了6.25%左右。     

一种基于小波子带熵的遥感图像压缩算法

提出了一种使用小波子带熵进行比特分配的遥感图像压缩算法.对遥感图像进行小波提升分解后,分析了各高频子带能量百分比及其熵的变化趋势,在此基础上提出了一种新的快速比特分配方法-使用子带熵进行比特分配.然后对各个高频子带进行均匀量化,量化后的数据采用比特平面编码.对最高比特平面只记录该比特平面中非零系数的坐标,其它比特平面采用行程编码和Huffman编码方法进行压缩.实验结果表明,纹理复杂以及相对平坦的遥感图像使用该算法压缩后都可以获得很好的重构图像质量,峰值信噪比均大于34dB,而压缩比则与图像的复杂程度有关.     

LSB扩展的图像自嵌入方法

本文描述了一种基于LSB扩展的图像自嵌入方法.该方法在使用LSB数据隐藏的同时,对图像的高层位平面采用无损数据嵌入方法,将图像的压缩信息与认证信息嵌入到图像自身中.当原图像有缺损或被篡改时,使用认证信息可较准确地定位受损位置;使用从偏移图像子块中提取的数据,可近似地恢复原图像的受损部分;同时图像的高层位平面还可无损恢复.该方法增加了数据嵌入的容量,提高了恢复图像的质量.     

基于生成对抗网络的压缩感知图像重构方法

目的为了解决传统压缩感知图像重构方法存在的重构时间长、重构图像质量不高等问题,提出一种基于生成对抗网络的压缩感知图像重构方法。方法基于生成对抗网络思想设计一种由具有稀疏采样功能的鉴别器和具有图像重构功能的生成器组成的深度学习网络模型,利用对抗损失和重构损失2个部分组成的新的损失函数对网络参数进行优化,完成图像压缩重构过程。结果实验表明,文中方法在12.5%的低采样率下重构时间为0.009s,相较于常用的OMP算法、CoSaMP算法、SP算法和IRLS算法,其峰值信噪比(PSNR)提高了10～12 dB。结论文中设计的方法应用于图像重构时重构时间短,在低采样率下仍能获得高质量的重构效果。     

基于图像感兴趣区域的图像压缩方法

目的提出一种基于图像感兴趣区域的图像压缩方式,实现在减少图像存储空间时图像失真少的效果。方法采用眼动仪提取图像感兴趣区域,制作压缩掩码对图像进行分区压缩,非感兴趣区域采用DCT算法进行压缩,而感兴趣区域不做任何压缩处理直接保留原样。结果主客观评价实验表明:压缩后的图像失真较少,视觉观察效果好,且压缩后所占存储空间减半,方法简便,压缩效率高。结论结合眼动仪提取感兴趣区域的压缩方法优于基于Itti视觉模型的压缩方法,适合压缩多种类型的图像,具有较好的实用性。     

分形图像压缩方法研究的新进展

分形图像压缩是一种利用迭代函数系统理论(IFS)、基于自相似特征的有损编码方法。它以其高压缩比的潜在性能而在近年来倍受重视，但目前实现自动IFS编码仍有相当难度，该领域仍存在许多问题亟待解决。笔者对分形图像压缩的理论基础、自动分形图像压缩的实现以及分形图像序列压缩等进行了全面的综述，介绍了各种具有代表性的改进算法，阐明了各个算法的原理和特点，最后对目前研究中存在的问题及可能的对策和研究方向进行了讨论。     

Significance of pre-processing and its impact on lossless hyperspectral image compression

The primitive aspect of hyperspectral imagery is its inherent spatial and spectral correlation. This correlation is exploited by subjecting the imaging cube to compression. A new approach to accomplish lossless hyperspectral image compression has been proposed. The imaging cube is subjected to pre-processing stage prior to entropy coding. Pre-processing stage comprises band normalization, ordering of bands followed by image scanning. A new sorting technique entitled Greedy Heap Sorting is suggested. The proposed strategy yields an average compression ratio (CR) of 4.93 and average bits per pixel (bpp) of 3.08. The proficiency of the system is on par with the existing contemporary algorithms for lossless hyperspectral image compression in terms of CR, bpp and reduced complexity.     

Lossless and lossy image compression using boolean function minimization

A novel approach for lossless as well as lossy compression of monochrome images using Boolean minimization is proposed. The image is split into bit planes. Each bit plane is divided into windows or blocks of variable size. Each block is transformed into a Boolean switching function in cubical form, treating the pixel values as output of the function. Compression is performed by minimizing these switching functions using ESPRESSO, a cube based two level function minimizer. The minimized cubes are encoded using a code set which satisfies the prefix property. Our technique of lossless compression involves linear prediction as a preprocessing step and has compression ratio comparable to that of JPEG lossless compression technique. Our lossy compression technique involves reducing the number of bit planes as a preprocessing step which incurs minimal loss in the information of the image. The bit planes that remain after preprocessing are compressed using our lossless compression technique based on Boolean minimization. Qualitatively one cannot visually distinguish between the original image and the lossy image and the value of mean square error is kept low. For mean square error value close to that of JPEG lossy compression technique, our method gives better compression ratio. The compression scheme is relatively slower while the decompression time is comparable to that of JPEG.     

A minute lossy method for 2D‐gel images compression

This article presents a compression method to encode a 2D‐gel image by using hybrid lossless and lossy techniques. In this method, areas containing protein spots are encoded using lossless method while the background is encoded using the lossy method. A 2D‐gel image usually covers a large portion of the background in which has colors that are close to white. The VQ codebook‐generating approach gives more codewords to describe the background; consequently, the proposed method can nearly precisely depict the background of the 2D‐gel image and exactly record protein spots without any losses. Therefore, it can provide a high compression ratio. Image compressed by this method can nearly be lossless reconstructed. The experimental results show that the compression ratio is significantly improved with acceptable image quality compared to the JPEG‐LS method. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 1–8, 2006     

一种改善无损压缩性能的预处理及其理论分析

在 JPEG2000 无损编码方案中增加一步预处理而提出了一种新的编码算法。这种算法在编码端增加一项简单的预处理运算,在解码端增加了逆预处理运算。算法十分简单,在时间上没有明显增加,而在压缩比上有一定程度的提高。试验结果表明,采用这种编码算法时,压缩比可以提高 20%;理论分析也证实,只要能够保证减小图像的整体方差就能改善压缩比。该预处理也可应用于其它无损压缩算法。     

Lossless compression-based progressive image transmission scheme

Abstract

The lossless compression of images is widely used in medical imaging and remote sensing applications. Also, progressive transmission of images is often desirable because it can reduce the transmission bits of an image. Therefore, combining the features of lossless compression and progressive transmission of images has been intensely researched. The bitplane method (BPM) is the simplest way to implement a lossless progressive image transmission system. In the present paper, a new block-based scheme for lossless progressive image transmission is proposed. This scheme will reduce the transmission load and improve the image quality of BPM. This method first performs a quantization operation upon the blocks of an image. Next, these blocks are encoded with fewer bits, and the bits are then transmitted phase by phase. The experimental results show that the image quality of this method is better than those in the BPM and improved BPM in related traditional works under the same transmission load. Moreover, during the first phase, the difference in peak signal-to-noise ratio between the present method and BPM is exactly equal up to 8.85 dB. This method is therefore effective for lossless progressive image transmission.     

采用小波与神经网络的高效图像数据压缩方案

通过构造特别的映射、整函数和BP神经网络,获得一套基于神经网络的无损数据压缩方案。由于该方案能压缩已被小波编码压缩过的数据,因此将其嵌套入一好的小波编码系统就可以获得一种基于小波与神经网络的高效图像数据压缩方案。实验证明,该高效方案对于Lenna图像的压缩比为43∶1, 并且恢复的图像有较好的视觉效果。     

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013     

A new shape-vector quantization-based adaptive predictive image coder

In this paper, a new lossless image compression technique, shape-vector quantization (VQ)-based adaptive predictive coder (SAPC), is introduced. In the proposed scheme, the local shape information of the image block is obtained through shape-VQ. This information is utilized by a novel predictive coder, shape-differential pulse code modulation (DPCM), to adaptively select the optimum predictor on a pixel-by-pixel basis. The prediction errors can be further compressed by an error-adjusting process. The proposed scheme achieves a breakthrough in prediction by utilizing the local feature of the image block through shape-VQ, thus improving the accuracy of the prediction while reducing the overhead of the side information. It also simplifies the complicated procedures involved in the computation of the prediction parameters. Although the proposed scheme outperforms many traditional lossless image-coding schemes, it produces comparable results to the newly developed context-based scheme with lower computational complexity. On the basis of the promising compression results, the proposed scheme could be the best candidate for the lossless image coding. © 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 10, 419–426, 1999     

CCD传感器噪声对遥感影像无损压缩的影响

为分析CCD传感器噪声对遥感影像无损压缩的影响,选取ISO/IEC标准图像和UK-DMC多光谱影像作为测试图像,根据CCD传感器噪声模型,在测试图像上添加CCD传感器模拟噪声,采用JPEG2000-Lossless无损压缩算法对不同污染程度的噪声图像进行压缩处理,并对不同污染程度的噪声影像无损压缩比进行比较分析.而后,在多光谱遥感影像上添加模拟的混合噪声,分析CCD噪声对星地间数据传输效率的影响.实验结果表明,CCD传感器噪声对所获取影像的污染会降低遥感影像的无损压缩比和星地间数据传输效率,以泊松噪声为模型的散粒噪声与读出噪声对影像无损压缩的影响最大,以高斯噪声为模型的热噪声与暗电流噪声的影响次之,由于CCD器件工艺问题引发的脉冲噪声对无损压缩的影响最小.