Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization

The enormous data of volumetric medical images (VMI) bring a transmission and storage problem that can be solved by using a compression technique. For the lossy compression of a very long VMI sequence, automatically maintaining the diagnosis features in reconstructed images is essential. The proposed wavelet-based adaptive vector quantizer incorporates a distortion-constrained codebook replenishment (DCCR) mechanism to meet a user-defined quality demand in peak signal-to-noise ratio. Combining a codebook updating strategy and the well-known set partitioning in hierarchical trees (SPIHT) technique, the DCCR mechanism provides an excellent coding gain. Experimental results show that the proposed approach is superior to the pure SPIHT and the JPEG2000 algorithms in terms of coding performance. We also propose an iterative fast searching algorithm to find the desired signal quality along an energy-quality curve instead of a traditional rate-distortion curve. The algorithm performs the quality control quickly, smoothly, and reliably.     

Motion compensated lossy-to-lossless compression of 4-D medical images using integer wavelet transforms.

This paper proposes a method for progressive lossy-to-lossless compression of four-dimensional (4-D) medical images (sequences of volumetric images over time) by using a combination of three-dimensional (3-D) integer wavelet transform (IWT) and 3-D motion compensation. A 3-D extension of the set-partitioning in hierarchical trees (SPIHT) algorithm is employed for coding the wavelet coefficients. To effectively exploit the redundancy between consecutive 3-D images, the concepts of key and residual frames from video coding is used. A fast 3-D cube matching algorithm is employed to do motion estimation. The key and the residual volumes are then coded using 3-D IWT and the modified 3-D SPIHT. The experimental results presented in this paper show that our proposed compression scheme achieves better lossy and lossless compression performance on 4-D medical images when compared with JPEG-2000 and volumetric compression based on 3-D SPIHT.     

基于改进SPIHT的医学图像压缩

为了满足医学数据的存储和传输的需要,在SPIHT算法的基础上,提出了一种基于改进SPIHT的医学图像压缩算法,实现了对医学图像的编码。该算法在减少编码时间的同时,能够得到较高的压缩比,重建图像的峰值信噪比也有明显的提高。选取几幅医学图像进行有效性的验证,试验结果表明:此算法可以更好地保留图像的客观质量,提高了编解码的效率,获得了较好的压缩效果。     

Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.     

支持可变分辨率的SPIHT编码算法

为了适应移动多媒体通信中终端设备显示尺寸多样性的要求,本文将图像缩放嵌入编码过程中,提出了一种基于线裁剪（seam carving, SC）的支持可变分辨率的多级树集合排序（set portioning in hierarchical trees,SPIHT）图像编码算法。该算法在构造新的基于根节点的空间方向树的基础上,利用SC生成基于块的线能量图来引导编码,获得空域可伸缩的码流;解码端仅需获取与终端设备显示尺寸相关的码流即可完成解码和图像缩放。实验结果表明,当编码和解码图像的分辨率一致时,本文算法的率失真（rate-distortion, R-D）性能逼近传统的SPIHT算法;当解码图像的分辨率可变时,本文算法在压缩码率与重建图像的主观质量上均优于传统的SPIHT算法。      

Adaptive compression of medical ultrasound images

An adaptive image-coding algorithm for compression of medical ultrasound (US) images in the wavelet domain is presented. First, it is shown that the histograms of wavelet coefficients of the subbands in the US images are heavy-tailed and can be better modelled by using the generalised Student's t-distribution. Then, by exploiting these statistics, an adaptive image coder named JTQVS-WV is designed, which unifies the two approaches to image-adaptive coding: rate-distortion (R-D) optimised quantiser selection and R-D optimal thresholding, and is based on the varying-slope quantisation strategy. The use of varying-slope quantisation strategy (instead of fixed R-D slope) allows coding of the wavelet coefficients across various scales according to their importance for the quality of reconstructed image. The experimental results show that the varying-slope quantisation strategy leads to a significant improvement in the compression performance of the JTQVS-WV over the best state-of-the-art image coder, SPIHT, JPEG2000 and the fixed-slope variant of JTQVS-WV named JTQ-WV. For example, the coding of US images at 0.5 bpp yields a peak signal-to-noise ratio gain of >0.6, 3.86 and 0.3 dB over the benchmark, SPIHT, JPEG2000 and JTQ-WV, respectively.     

Near-lossless compression of medical images through entropy-coded DPCM

The near-lossless, i.e., lossy but high-fidelity, compression of medical Images using the entropy-coded DPCM method is investigated. A source model with multiple contexts and arithmetic coding are used to enhance the compression performance of the method. In implementing the method, two different quantizers each with a large number of quantization levels are considered. Experiments involving several MR (magnetic resonance) and US (ultrasound) images show that the entropy-coded DPCM method can provide compression in the range from 4 to 10 with a peak SNR of about 50 dB for 8-bit medical images. The use of multiple contexts is found to improve the compression performance by about 25% to 30% for MR images and 30% to 35% for US images. A comparison with the JPEG standard reveals that the entropy-coded DPCM method can provide about 7 to 8 dB higher SNR for the same compression performance.     

Listless block-tree set partitioning algorithm for very low bit rate embedded image compression

This paper presents a listless implementation of wavelet based block tree coding (WBTC) algorithm of varying root block sizes. WBTC algorithm improves the image compression performance of set partitioning in hierarchical trees (SPIHT) at lower rates by efficiently encoding both inter and intra scale correlation using block trees. Though WBTC lowers the memory requirement by using block trees compared to SPIHT, it makes use of three ordered auxiliary lists. This feature makes WBTC undesirable for hardware implementation; as it needs a lot of memory management when the list nodes grow exponentially on each pass. The proposed listless implementation of WBTC algorithm uses special markers instead of lists. This reduces dynamic memory requirement by 88% with respect to WBTC and 89% with respect to SPIHT. The proposed algorithm is combined with discrete cosine transform (DCT) and discrete wavelet transform (DWT) to show its superiority over DCT and DWT based embedded coders, including JPEG 2000 at lower rates. The compression performance on most of the standard test images is nearly same as WBTC, and outperforms SPIHT by a wide margin particularly at lower bit rates.     

一种新颖的中子辐照图像压缩算法

分析了中子辐照图像的持点,提出一种适合于数字中子照相系统的图像压缩算法。将小波和方向滤波器组（DFB）结合,得到非冗余的图像变换WDFB;通过WDFB系数的重新组合,提出一种新颖的图像压缩方案,该方案类似于无表零树编码（LZC）算法;为了充分利用WDFB变换对二维分段光滑函数的良好表达能力,该算法引入线性索引技术并采用不同于LZC算法的零树表达策略。实验结果表明,该算法用于中子辐照图像压缩是有效的,在相同压缩比下,其主观视觉质量和峰值信噪比（PSNR）都明显优于LZC算法,和多级树集合分裂编码（SPIHT）算法相比,具有相当的压缩性能但更易于硬件实现。     

结合矢量量化的SPIHT算法用于多光谱图像压缩

针对多波段遥感图像纹理复杂丰富、局部相关性较弱的特点,提出了结合矢量量化的SPIHT压缩算法。将经过小波变换后的遥感图像谱间相同位置的系数聚集构成矢量,根据高频子图的局部块纹理强弱进行自适应性的量化。使基于标量的SPIHT算法能够方便的处理矢量,有效去除数据间各类相关。实验表明,该方法对多波段遥感图像的压缩可以收到良好的效果,且算法具有良好的实时性,对单幅图像的压缩比和峰值信噪比(PSNR)均优于普通的二维SPIHT算法。     

基于3维SPIHT编码的超光谱图像压缩

提出一种针对超光谱图像压缩的3维SPIHT编码算法.通过对超光谱图像进行3维小波变换,同时去除像素数据间的空间冗余和谱间冗余.针对变换后得到的小波系数,构造一种3维空间方向树结构,并用经3维扩展后的SPIHT算法(3D SPIHT算法)对小波系数进行量化编码.实验证明,基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时,表现出了优良的率失真性能.并且算法复杂度适中,具有嵌入式特性.     

Universal perceptual weighted zerotree coding for image and videocompression

A universal representation for the perceptual weighted zerotree coding algorithm is developed, in which the perceptual weighted zerotree coding is decomposed into two separate parts, i.e. visual weighting and zerotree representation, which can be realised independently. Prior to zerotree processing, the extracted full-tree is weighted by using a visual weighting matrix. Any zerotree algorithm like EZW, SPIHT and zerotree space-frequency quantisation can be used to encode the weighted coefficients of the wavelet transform. In other words, any previous algorithm without perceptual weighting can be easily extended to form a new perceptual coder using the proposed framework. Several examples of visual weighting matrices are given to show the effect of the new method     

Error-resilient SPIHT image coding

The authors develop an efficient error-resilient scheme for the set partitioning in hierarchical trees (SPIHT) technique, one of the most successful image compression algorithms. By partitioning the coded data sequence and adding appropriate side information, the proposed algorithm provides significantly better PSNR performance over noisy channels with a minimal increase in the coding complexity     

一种基于低复杂度嵌入零块编码的遥感图像压缩编码方法

该文针对遥感图像的数据特点,提出了一种新的遥感图像编码方法。它基于一种改进的小波变换嵌入零块编码算法。新算法中改进了零块编码中四叉树分裂算法(quadtree),并设计亍新的链表生成和不重要集合排序策略。通过这些方法的改进,不仅提高了图像编码性能,同时还大大提高了运算效率。实验表明该文阐述的算法具有很低的复杂度和高的压缩率,PSNR和计算速度均超过SPIHT和SPECK。在1bpp下,该文方法的PSNR比SPIHT提高了0.3dB以上,计算速度比SPIHT提高了35％。     

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。     

Low-bit-rate coding of underwater color image using improved wavelet difference reduction

Underwater image compression has been the key technology for transmitting massive amount of image data via underwater acoustic channel with limited bandwidth. According to the characteristics of underwater color images, an efficient underwater image compression method has been developed. The new coding scheme employs a wavelet-based preprocessing method to remove the visual redundancy, and adopts a Wavelet Tree-based Wavelet Difference Reduction (WTWDR) algorithm to remove the spatial redundancy of underwater color images. Instead of scanning whole transformed image like the WDR method, the difference reduction coding is used for each significant wavelet tree in the proposed WTWDR algorithm based on the correlation between the subbands of higher levels and lower levels of a transformed image. The experimental results show that for underwater color images the proposed method outperforms both WDR and SPIHT at very low bit rates in terms of compression ratio and reconstructed quality, while for natural images it has similar performance with WDR and SPIHT. Hence, the proposed approach is especially suitable for underwater color image compression at very low bit rates.     

Compression of multispectral images by three-dimensional SPIHTalgorithm

The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3D) transform is taken (wavelet in the spatial domain, Karhunen-Loeve in the spectral domain) and a simple 3D SPIHT is used; in the second, after taking a spatial wavelet transform, spectral vectors of pixels are vector quantized and a gain-driven SPIHT is used. Numerous experiments on two sample multispectral images show very good performance for both algorithms     

Wavelet tree classification and hybrid coding for image compression

A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image.     

A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT

Due to its excellent rate–distortion performance, set partitioning in hierarchical trees (SPIHT) has become the state-of-the-art algorithm for image compression. However, the algorithm does not fully provide the desired features of progressive transmission, spatial scalability and optimal visual quality, at very low bit rate coding. Furthermore, the use of three linked lists for recording the coordinates of wavelet coefficients and tree sets during the coding process becomes the bottleneck of a fast implementation of the SPIHT. In this paper, we propose a listless modified SPIHT (LMSPIHT) approach, which is a fast and low memory image coding algorithm based on the lifting wavelet transform. The LMSPIHT jointly considers the advantages of progressive transmission, spatial scalability, and incorporates human visual system (HVS) characteristics in the coding scheme; thus it outperforms the traditional SPIHT algorithm at low bit rate coding. Compared with the SPIHT algorithm, LMSPIHT provides a better compression performance and a superior perceptual performance with low coding complexity. The compression efficiency of LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information belonging to the lower frequency bands earlier in the bit stream than that of the SPIHT to better exploit the energy compaction of the wavelet coefficients. HVS characteristics are employed to improve the perceptual quality of the compressed image by placing more coding artifacts in the less visually significant regions of the image. Finally, a listless implementation structure further reduces the amount of memory and improves the speed of compression by more than 51% for a 512×512 image, as compared with that of the SPIHT algorithm.