分形与图象压缩

分形几何方法是一种新的图象压缩方法.本文介绍了分形几何和分形用于图象压缩的一些基本原理,以及以分形为基础的几种图象压缩编码方法.     

一种基于分形维数的图像压缩编码方法

本文提出了一种基于分形维数的ＤＣＴ变换压缩编码与ＩＦＳ分形编码相结合的图象压缩方法。实验结果表明,与基本的自动分形图象编码方法相比,运算时间下降而压缩比和信噪比提高。     

利用短信息传送静态数字图象

在对 CDMA蜂房移动通信系统短消息中心进行研究的基础上 ,对通过短信息的格式传输静态数字图象的可行性作了探讨。在静态数字图象的压缩和编码过程中 ,采用 JPEG压缩和基于小波分解与分形编码混合的图象压缩编码方法。     

一种基于局部极大模和分形的混合编码方法

在分析分形理论和小波变换模极大值用于图象压缩方面优缺点的基础上 ,根据它们的特点 ,综合利用两种方法的优点 ,弥补它们不足 ,提出了一种基于小波变换极大模和分形的混合编码方法 .该方法先利用小波局部模极大值提取的图象边缘特性来进行图象编码 ,然后用分形的自相似性对图象的误差图进行压缩 ,以获得高压缩比和质量好的恢复图象 ;同时还简单进行了理论阐述和给出了具体算法的步骤 .实验结果表明 ,采用此方法压缩后的图象不仅视觉效果好、无方块效应 ,而且边缘无振荡现象、图象清晰无模糊 ,压缩比高 .     

序列图象的似分形编码方法

该文将目前分形图象编码中描述图象自相似性的方法,用于对序列图象中图象间互相似性的描述,并提出了一种称为似分形的序列图象编码方法。与传统的分形编码方法相比,这种方法由于在编码时不受分形编码特有条件的限制,解码时也不需要迭代,因此编码速度和编码质量都得到一定程度的提高。实验结果证明了该方法的有效性。     

基于视觉灵敏度分类的IFS自适应图象编码算法

为了提高IFS自适应图象压缩编码方法对不同图象的适应能力,在按人类视觉对比灵敏度分类的基础上,提出了一种进行分形图象IFS自适应压缩编码的新算法,同时定义了广义置信度的概念,并根据MSE误差曲线的广义置信度,确定了基于四叉树结构的分块分形编码过程中的匹配误差门奶。这种自适应门限编码算法不仅克服了传统固定门限IFS编码算法不能很地适应不同复杂程度输入图象的缺点,而且还提高了编码的效率。实验结果证明,此算法不仅可以自动地适应不同的输入图象,而且解码图象的视觉效果良好。     

结合小波变换的零搜索分形图象编码

为提高分形图象编码的质量,缩短编码时间,针对Monro在文献[1]中提出的零搜索分形图象编码方法,其恢复图象在存在的块效应的问题,提出了一种结合小波变换的多项式近似快速分形图象编码方法,该方法是利用各频带间能量分布不均衡的特性,构造一种结合小波分解的分形图象编码算法,首先对图象进行塔式离散正交小波变换,然后再对小波系数进行分形编码,实验结果表明,用该算法对图象进行编码,不仅使恢复图象的质量得到了较大的提高,而且编码时间仅用1．48s。     

基于小波变换的分形预测图象编码方法

提出一种把小波图象编码与分形图象编码相结合的新的编码方法。图象经过小波分解后,呈现出两种相似性;一种是图象本身的自相似性,一是同方向不同分辨率子图象之间的互相似性。利用第二种相似性,也即利用低分辩率子图象为同方向讷经子图象进行分形预测编码。由于小波分解后低分辨率子图象的范围比高分辨率子图象的小一倍,故该编码一般的分形编码方法大大缩短了编码的时间,就编码的效果而言,该方法也比较令人满意。     

一种基于小波变换的分形零树混合编码方法

为了降低分形编码的复杂度和提高编码效率 ,提出了一种基于小波变换的分形零树混合图象编码方法 .该方法首先利用小波变换将图象分解为不同方向不同分辨率的子图象 ,然后分别在不同分辨率层将这些子图象以类似于零树的结构 ,构成一棵棵的小波子树 (wavelet subtree) ,最后在编码时 ,对每一棵小波子树进行具体的分析 ,或采用分形编码 (fractal coding) ,或采用零树编码 (zerotree coding) .与传统的基于小波变换的分形编码相比 ,此算法在充分利用子带图象间的相似性和块内存在的自相似性的同时 ,也充分利用了小波变换后子图象块内 ,特别是高分辨层的子图象块内所存在的大量局域性的冗余性 .实验结果表明 ,此方法在较大的压缩范围内 ,都能够获得较好的压缩效果 .     

XYZ实时视频编码技术

考虑到工业现场中特有的传感器测量环境,提出了利用传感器信息压缩工业图象的观点,在此基础上,提出了基于信息融合的工业运动视频图象压缩编码方法,并对基于模型的信息融合编码方法压缩运输皮带图象进行了研究,压缩效果使图象传输的码速率降低到１－２Ｋｂｐｓ     

Fractal volume compression

This research explores the principles, implementation, and optimization of a competitive volume compression system based on fractal image compression. The extension of fractal image compression to volumetric data is trivial in theory. However, the simple addition of a dimension to existing fractal image compression algorithms results in infeasible compression times and noncompetitive volume compression results. This paper extends several fractal image compression enhancements to perform properly and efficiently on volumetric data, and introduces a new 3D edge classification scheme based on principal component analysis. Numerous experiments over the many parameters of fractal volume compression suggest aggressive settings of its system parameters. At this peak efficiency, fractal volume compression surpasses vector quantization and approaches within 1 dB PSNR of the discrete cosine transform. When compared to the DCT, fractal volume compression represents surfaces in volumes exceptionally well at high compression rates, and the artifacts of its compression error appear as noise instead of deceptive smoothing or distracting ringing     

An adaptive technique to maximize lossless image data compression of satellite images

Data compression will play an increasingly important role in the storage and transmission of image data within the NASA science programs as the Earth Observing System comes into operation. It is important that the science data be preserved at the fidelity the instrument and satellite communication systems were designed to produce. Lossless compression must therefore be applied, at least to archive the processed instrument data. In this paper, we present an analysis of the performance of lossless compression techniques and develop an adaptive approach that applies image remapping, feature-based image segmentation to determine regions of similar entropy, as well as high-order arithmetic coding, to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi-linear interpolation, and block-based linear predictive coding. The results of these experiments are discussed, and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties and then applying a coding technique that maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. The paper concludes that maximum compression can be achieved cost effectively and at acceptable performance rates with a combination of techniques that are selected based on image contextual information.     

端到端优化的图像压缩技术进展

图像压缩是数据压缩技术在数字图像上的应用,其目的是减少图像数据中的冗余,从而用更加高效的格式存储和传输数据.传统的图像压缩方法中,图像压缩分为预测、变换、量化、熵编码等步骤,每一步均采用人工设计的算法分别进行优化.近年来,基于深度神经网络的端到端图像压缩方法在图像压缩中取得了丰硕的成果,相比传统方法,端到端图像压缩可以...     

A secret image sharing scheme based on piecewise linear chaotic map and Chinese remainder theorem

In this paper, a secret image sharing scheme, by combining arithmetic compression coding and Chinese remainder theorem (CRT) is proposed. It is well known that arithmetic compression coding method for image has a good compressibility, and it can reduce the size of the shadow image, which consists of sharing values. Usually, a smaller shadow image is convenient to store and transmit. The piecewise linear map is applied to design compression coding scheme, which has the same properties as the conventional arithmetic compression coding. The CRT is used to construct the sharing scheme for compression codes. Meanwhile, it also has encryption effects in the process of sharing. Finally, the security and the effectiveness of the secret image sharing scheme are confirmed by some computer simulation results.

     

变步长和变阈值的分形小波图像压缩算法

针对目前分形小波混合图像压缩方法存在的不足,提出了一种新的分形小波混合图像压缩方法。同方向、不同分辨率的相邻子带图像之间进行分形搜索时,根据其系数的重要程度不同,采用不同的误差阈值与搜索步长,高分辨率子带的搜索步长大,误差阈值小,而低分辨率的子带则采用小步长和大误差阈值进行搜索。实验表明,相对于经典的基于小波树的分形图像压缩方法,该改进算法在不影响信噪比和解码图像质量的前提下,提高了压缩比和编码速度。     

基于边缘检测的噪声图像压缩编码方法

基于小波变换的图像压缩算法在含噪图像和较低码率时出现的边缘模糊现象多年来一直未能得到很好的解决,为了解决这一问题,提出一种基于边缘检测的噪声图像压缩编码方法。首先,对图像进行小波边缘检测,确定哪些小波系数是图像的边缘特征,将其保护起来;然后,对小波变换域系数采用软阈值收缩方法实现去噪;最后,利用等级树集合分裂算法(SPIHT)算法对图像进行压缩编码。实验结果表明,本文提出的方法不仅能获得较高的图像压缩率、较好地去除噪声,而且能在一定程度上解决边缘模糊问题。     

Using tchebichef moment for fast and efficient image compression

Orthogonal moment is known as better moment functions compared to the non-orthogonal moment. Among all the orthogonal moments, Tchebichef Moment appear to be the most recent moment functions that still attract the interest among the computer vision researchers. This paper proposes a novel approach based on discrete orthogonal Tchebichef Moment for an efficient image compression. The image compression is useful in many applications especially related to images that are needed to be seen in small devices such as in mobile phone. Meanwhile, the method incorporates simplified mathematical framework techniques using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. In addition, a comparison between Tchebichef Moment compression and JPEG compression is conducted. The result shows significant advantages for Tchebichef Moment in terms of its image quality and compression rate. Tchebichef moment provides a more compact support to the image via sub-block reconstruction for compression. Tchebichef Moment Compression is able to perform potentially better for a broader domain on real digital images and graphically generated images.     

改进的广义置信度自适应IFS图象压缩编码算法

通过对分块IFS图象压缩编码过程中匹配误差的分析,提出了广义置信度的概念,并据此提出了对输入图象进行四叉树分块的自适应匹配门限（AT）算法。根据排列块的相对复杂程度,修正了自适应匹配门限的公式,提出了改进的自适应门限（RAT）IFS图象压缩编码算法。在对输入图象进行四叉树分块编码过程中,该方法可以根据当前排列块的统计特征确定匹配门限,从而使分块编码过程自动地适应输入图象。实验结果表明,这种新的编码方法可以自适应地对输入图象进行编码,且压缩比较高,有一定的实用性。     

VLSI based orthogonal diagonal cross hair search (ODCHS) algorithm implementation for efficient image compression

In this paper, an efficient image codec is proposed using Magnetic Resonance Images (MRI). During the past few years, frequency domain analyzes such as Discrete Cosine Transform and Discrete Wavelet Transform (DWT) have been widely used in the field of image compression due to their well localized property of its coefficients in both frequency and space domain. This work also deals with image compression based on frequency domain transformation. As the medical images are very important for diagnosis, they require lossless compression to store them. However, the coefficients of DWT are real numbers; lossless compression cannot be achieved. To overcome this limitation, a variant of DWT named Lifting Wavelet Transform (LWT) is utilized in the proposed system. The proposed codec is applied on the decomposed image. The codec has also been synthesized using FPGA and the results are compared with simulation results and verified.