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.     

Three-dimensional image compression with integer wavelet transforms

A three-dimensional (3-D) image-compression algorithm based on integer wavelet transforms and zerotree coding is presented. The embedded coding of zerotrees of wavelet coefficients (EZW) algorithm is extended to three dimensions, and context-based adaptive arithmetic coding is used to improve its performance. The resultant algorithm, 3-D CB-EZW, efficiently encodes 3-D image data by the exploitation of the dependencies in all dimensions, while enabling lossy and lossless decompression from the same bit stream. Compared with the best available two-dimensional lossless compression techniques, the 3-D CB-EZW algorithm produced averages of 22%, 25%, and 20% decreases in compressed file sizes for computed tomography, magnetic resonance, and Airborne Visible Infrared Imaging Spectrometer images, respectively. The progressive performance of the algorithm is also compared with other lossy progressive-coding algorithms.     

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     

3D multiwavelet based block coding algorithm for compression of volumetric medical images

Three dimensional (3D) medical images possess some specific characteristics that should be utilized by an efficient compression scheme. In this article, one such compression scheme for volumetric 3D medical image data is presented. Two processes involved in this scheme are decorrelation and encoding. Decorrelation of the 3D data is realized through 3D multiwavelet transform with apt prefiltering so as to give good representation of the image which could be exploited by the encoder. Encoding is done through proposed Block Coding Algorithm, which is embedded, block based, and wavelet transform coding algorithm without maintaining any list structures. The idea behind this algorithm is to sort the 3D transform coefficients in to a 1D array with respect to declining thresholds and to use state table to keep track of the blocks and coefficients that has been coded. In the experiment conducted on various 3D magnetic resonance and computed tomography images of human brain with multiwavelets such as Geronimo–Hardin–Massopust, Chui‐Lian, and orthogonal symmetric/antisymmetric (SA4), efficiency of the proposed scheme was weighed against the state of art encoders such as 3D Set Partitioning in Hierarchical Trees, 2D Set Partitioned Embedded BloCK Coder, and No List SPIHT. Attributes used for performance measurements are peak signal to noise ratio, bit rate, and structural similarity index of reconstructed image with respect to original image. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 182–192, 2014     

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。     

ROI-based DICOM image compression for telemedicine

Many classes of images contain spatial regions which are more important than other regions. Compression methods capable of delivering higher reconstruction quality for important parts are attractive in this situation. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Hence, Region Based Coding (RBC) technique is significant for medical image compression and transmission. Lossless compression schemes with secure transmission play a key role in telemedicine applications that help in accurate diagnosis and research. In this paper, we propose lossless scalable RBC for Digital Imaging and Communications in Medicine (DICOM) images based on Integer Wavelet Transform (IWT) and with distortion limiting compression technique for other regions in image. The main objective of this work is to reject the noisy background and reconstruct the image portions losslessly. The compressed image can be accessed and sent over telemedicine network using personal digital assistance (PDA) like mobile.     

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。     

Efficient colour image compression using fusion approach

The aim of image compression endeavour is to reduce the total data required to represent the image, which, in turn, decreases the demand of transmission bandwidth and storage space. In this work, we propose an image fusion based idea that can be exploited extensively to reduce the file size of JPEG compressed image further. Before performing the JPEG compression, we compute both intensity and a subsampled colour representation of the image undergoing compression. Then, similar to the JPEG compression, discrete cosine transformation, quantisation and entropy coding processes are applied on these images and stored in a single image file container. In the decoder, these two images are reconstructed and fused to obtain the resultant decoded image. Our experiments show that the proposed method does meet the lower storage and bandwidth requirement by reducing the average bits per pixel of the encoded image than that of the JPEG compressed image.     

REPro.JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images

Medical images are known for their huge volume which becomes a real problem for their archiving or transmission notably for telemedicine applications. In this context, we present a new method for medical image compression which combines image definition resizing and JPEG compression. We baptise this new protocol REPro.JPEG (reduction/expansion protocol combined with JPEG compression). At first, the image is reduced then compressed before its archiving or transmission. At last, the user or the receiver decompresses the image then enlarges it before its display. The obtain results prove that, at the same number of bits per pixel lower than 0.42, that REPRo.JPEG guarantees a better preservation of image quality compared to the JPEG compression for dermatological medical images. Besides, applying the REPRo.JPEG on these colour medical images is more efficient while using the HSV colour space compared to the use of RGB or YCbCr colour spaces.     

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。     

Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

Image compression is a process based on reducing the redundancy of the image to be stored or transmitted in an efficient form. In this work, a new idea is proposed, where we take advantage of the redundancy that appears in a group of images to be all compressed together, instead of compressing each image by itself. In our proposed technique, a classification process is applied, where the set of the input images are classified into groups based on existing technique like L₁ and L₂ norms, color histograms. All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook. In the process of extracting the different sub-images, we used the mean squared error for comparison and three blurring methods (simple, middle and majority blurring) to increase the compression ratio. Experiments show that varying blurring values, as well as MSE thresholds, enhanced the compression results in a group of images compared to JPEG and PNG compressors.     

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.     

油气管道缺陷漏磁图像的小波压缩技术研究

针对漏磁信号的特点,提出了基于小波分析的缺陷漏磁图像压缩方案。该方案通过调整正交函数零极点位置来设计小波基函数,利用该函数对图像作小波变换,对获得的各级小波系数用更新的JPEG图像压缩表阈值量化,再用算术编码方案处理量化结果,从而得到压缩的图像。试验结果表明,当压缩比小于30%时,图像压缩引起的失真不会对缺陷分析产生影响。     

An efficient approach for brain image (tissue) compression based on the position of the brain tumor

Medical image processing plays an important role in brain tissue detection and segmentation. In this paper, a computer aided detection of brain tissue compression based on the estimation of the location of the brain tumor. The proposed system detects and segments the brain tissues and brain tumor using mathematical morphological operations. Further, the brain tissue with tumor is compressed using lossless compression technique and the brain tissue without tumor is compressed using lossy compression technique. The proposed method achieves 96.46% sensitivity, 99.20% specificity and 98.73% accuracy for the segmentation of white matter regions from the brain. The proposed method achieves 98.16% sensitivity, 99.36% specificity and 98.78% accuracy for the segmentation of cerebrospinal fluid (CSF) regions from the brain and also achieves 93.07% sensitivity, 98.79% specificity and 97.63% accuracy for the segmentation of grey matter regions from the brain. This paper focus the brain tissue compression based on the location of brain tumor. The grey matter of the brain is applied to lossless compression due to the presence of the tumor in grey matter of the brain. The proposed system achieves 29.23% of compression ratio for compressing the grey matter of the brain region. The white matter and CSF regions of the brain are applied to lossy compression due to the non‐presence of the tumor. The proposed system achieves 39.13% of compression ratio for compressing the white matter and also achieves 37.5% of compression ratio for compressing the CSF tissue. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 237–242, 2016     

Dimensional scalable lossless compression of MRI images using haar wavelet lifting scheme with EBCOT

Modern medical imaging requires storage of large quantities of digitized clinical data. To provide high bandwidth and to reduce the storage space, a medical image must be compressed before transmission. One of the best image compression techniques is using the Haar wavelet transform. The method of discrete cosine transform (DCT) is chosen to be the preprocessing scheme to identify the image frequency information and has excellent energy compaction property. The block coding algorithm uses a wavelet transform to generate the sub band samples, which can be quantized and coded. It is more robust to errors than many other wavelet‐based schemes. In this article, simulations are carried out on different medical Images and it demonstrates the performance in terms of peak signal to noise ratio (PSNR) & bits per pixel (BPP). Our proposed method is found to preserve information fidelity while reducing the amount of data. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 175–181, 2014     

A new progressive coding scheme for halftone dithered images using bit‐interleaving

A novel progressive coding scheme is presented for the efficient display of dithered images. Dithered images are the results of thresholding original gray‐level images with dithering screens. After the preprocessing of bit‐interleaving, this algorithm utilizes the characteristic of reordered image to determine the transmitting order and then progressively reconstructs the dithered image. In addition, the dithered images are further compressed by lossy and lossless procedures. The experimental results demonstrate high‐quality reconstructions while maintaining low transmitted bit rates. Moreover, an objective error criterion obtained by LMS optimization is also presented in this study. As documented in the experiments, the error criterion is quite consistent to the basic characteristic of human visual system. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 121–127, 2006.     

用于星上图像压缩的JPEG2000算法的改进

本文修改了 JPEG2000 算法,通过改变编码顺序节约了大量的计算,提高了运行速度.用此算法对空间太阳望远镜的目标图像进行了实验,结果表明在相同压缩比的情况下,修改后的算法对图像质量没有明显的影响,在运行时间和压缩质量方面都能够满足空间太阳望远镜的压缩任务要求.     

The new block pixel sort algorithm for TVC-encrypted medical image

To transfer the medical image from one place to another place or to store a medical image in a particular place with secure manner has become a challenge. In order to solve those problems, the medical image is encrypting and compressing before sending or saving at a place. In this paper, a new block pixel sort algorithm has been proposed for compressing the encrypted medical image. The encrypted medical image acts as an input for this compression process. During the compression, encrypted secret image E₁₂(;) is compressed by the pixel block sort encoding (PBSE). The image is divided into four identical blocks, similar to 2×2 matrix. The minimum occurrence pixel(s) are found out from every block and the positions of the minimum occurrence pixel(s) are found using the verdict occurrence process. The pixel positions are shortened with the help of a shortening process. The features (symbols and shortened pixel positions) are extracted from each block and the extracted features are stored in a particular place, and the values of these features put together as a compressed medical image. The next process of PBSE is pixel block short decoding (PBSD) process. In the decoding process, there are nine steps involved while decompressing the compressed encrypted medical image. The feature extraction value of compressed information is found out from the feature extraction, the symbols are split and the positions are shortened in a separate manner. The position is retrieved from the rescheduled process and the symbols and reconstructed positions of the minimum occurrence pixels are taken block wise. Every symbol is placed based on the position in each block: if the minimum occurrence pixel is ‘0’, then the rest of the places are automatically allocated as ‘1’ or if the minimum occurrence pixel is ‘1’ the remaining place is automatically allocated as ‘0’. Both the blocks are merged as per order 2×2. The final output is the reconstructed encrypted medical image. From this compression method, we can achieve the high compression ratio, minimum time, less compression size and lossless compression, which are the things experimented and proved.     

JPEG2000中可逆整数5/3小波变换及其在DSP上的实现

JPEG2000中的可逆小波变换利用整数到整数的小波变换技术。这种变换通过一维双通道完 全重构通用极大值滤波带实现,对图像块进行5/3滤波,获得的低通信号进行再滤波,得到的系数通过组合就成为JPEG2000文件数据。通过TMS320VC5402芯片实现这种滤波并优化汇编程序设计。该技术可实现对图像的有损和无损压缩,在其他图像处理领域也可以直接利用。