Chaos-based encryption for fractal image coding

A chaos-based cryptosystem for fractal image coding is proposed. The Renyi chaotic map is employed to determine the order of processing the range blocks and to generate the keystream for masking the encoded sequence. Compared with the standard approach of fraetal image coding followed by the Advanced Encryption Standard, our scheme offers a higher sensitivity to both plaintext and ciphertext at a comparable operating efficiency. The keystream generated by the Renyi chaotic map passes the randomness tests set by the United States National Institute of Standards and Technology, and so the proposed scheme is sensitive to the key.

An Adaptive Block-Based Fractal Image Coding Scheme

Based on Jacquin's work. this paper presents an adaptive block-based fractal image coding scheme. Firstly. masking functions are used to classify range blocks and weight the mean Square error (MSE) of images. Secondly, an adaptive block partition scheme is introduced by developing the quadtree partition method. Thirdly. a piecewise uniform quantization strategy is appled to quantize the luminance shifting. Finally. experiment results are shown and compared with what reported by Jacquin and Lu to verify the validity of the methods addressed by the authors.

A Compression Algorithm of Hyperspectral Remote Sensing Image Based on 3-D Wavelet-Fractal Coder

In this paper, the 3-D Wavelet-Fractal coder was used to compress the hyperspectral remote sensing image, which is a combination of 3-D improved set partitioning in hierarchical trees （SPIHT） coding and 3-D fractal coding. Hyperspectral image date cube was first translated by 3-D wavelet and the 3-D fractal compression ceding was applied to lowest frequency subband. The remaining coefficients of higher frequency sub-bands were encoding by 3-D improved SPIHT. We used the block set instead of the hierarchical trees to enhance SPIHT＇s flexibility. The classical eight kinds of affme transformations in 2-D fractal image compression were generalized to nineteen for the 3-D fractal image compression. The new compression method had been tested on MATLAB. The experiment results indicate that we can gain high compression ratios and the information loss is acceptable.

Dynamic Fractal Transform with Applications to Image Data Compression

A recent trend in computer graphics and image processing is to use Iterated Function System (IFS) to generate and describe both man-made graphics and natural images. Jacquin was the first to propose a fully automatic gray scale image compression algorithm which is referred to as a typical static fractal transform based algorithm in this paper. By using this algorithm, an image can be condensely described as a fractal transform operator which is the combination of a set of fractal mappings. When the fractal transform operator is iteratedly applied to any initial image, a unique attractor (reconstructed image) can be achieved. In this paper) a dynamic fractal transform is presented which is a modification of the static transform. Instead of being fixed, the dynamic transform operator varies in each decoder iteration, thus differs from static transform operators. The new transform has advantages in improving coding efficiency and shows better convergence for the decoder.

Fractal Block Coding:A New Method

Fractal image compression is a completely new method to compress images by searching and exploiting the self similarity of the whole image . Fractal Block Coding (FBC) is a practicable fractal coding scheme with annoying slow encoding speed . In this paper, we classify the image blocks by Classified Vector Quantization (CVQ) technique and present an Adaptive Block Truncation Coding (ABTC) scheme to process the midrange blocks in the image. By this method , we reduce the encoding time to one forty fifth comparing to ordinary FBC method with little change in compression ratio and a little decreased coded image quality.

A hyperspectral image compression algorithm based on wavelet transformation and fractal composition (AWFC)

Starting with a fractal-based image-compression algorithm based on wavelet transformation for hyperspectral images, the authors were able to obtain more spectral bands with the help of of hyperspectral remote sensing. Because large amounts of data and limited bandwidth complicate the storage and transmission of data measured by TB-level bits, it is important to compress image data acquired by hyperspectral sensors such as MODIS, PHI, and OMIS; otherwise, conventional lossless compression algorithms cannot reach adequate compression ratios. Other loss-compression methods can reach high compression ratios but lack good image fidelity, especially for hyperspectral image data. Among the third generation of image compression algorithms, fractal image compression based on wavelet transformation is superior to traditional compression methods,because it has high compression ratios and good image fidelity, and requires less computing time. To keep the spectral dimension invariable, the authors compared the results of two compression algorithms based on the storage-file structures of BSQ and of BIP, and improved the HV and Quadtree partitioning and domain-range matching algorithms in order to accelerate their encode/decode efficiency. The authors' Hyperspectral Image Process and Analysis System (HIPAS) software used a VC++6.0 integrated development environment (IDE), with which good experimental results were obtained. Possible modifications of the algorithm and limitations of the method are also discussed.

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.

Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

An image compression method for space multispectral time delay and integration charge coupled device camera

Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.

Wavelet-Based Mixed-Resolution Coding Approach Incorporating with SPT for the Stereo Image

With the advances of display technology, three-dimensional(3-D) imaging systems are becoming increasingly popular. One way of stimulating 3-D perception is to use stereo pairs, a pair of images of the same scene acquired from different perspectives. Since there is an inherent redundancy between the images of a stereo pairs, data compression algorithms should be employed to represent stereo pairs efficiently. The proposed techniques generally use block-based disparity compensation. In order to get the higher compression ratio, this paper employs the wavelet-based mixed-resolution coding technique to incorporate with SPT-based disparity-compensation to compress the stereo image data. The mixed-resolution coding is a perceptually justified technique that is achieved by presenting one eye with a low-resolution image and the other with a high-resolution image. Psychophysical experiments show that the stereo image pairs with one high-resolution image and one low-resolution image provide almost the same stereo depth to that of a stereo image with two high-resolution images. By combining the mixed-resolution coding and SPT-based disparity-compensation techniques, one reference (left) high-resolution image can be compressed by a hierarchical wavelet transform followed by vector quantization and Huffman encoder. After two level wavelet decompositions, for the low-resolution right image and low-resolution left image, subspace projection technique using the fixed block size disparity compensation estimation is used. At the decoder, the low-resolution right subimage is estimated using the disparity from the low-resolution left subimage. A full-size reconstruction is obtained by upsampling a factor of 4 and reconstructing with the synthesis low pass filter. Finally, experimental results are presented, which show that our scheme achieves a PSNR gain (about 0.92dB) as compared to the current block-based disparity compensation coding techniques.

Wavelet Coding of Image Using Quadtree and Vector Entropy Coding

A new efficient method based on Quadtree Representation and Vector Entropy Coding (QRVEC) for encoding the wavelet transform coefficients of images is presented. In addition, how to flexibly control the coder’ s output bit rate is also investigated.

高清图像通信中的带宽优化策略

文章探讨高清图像通信中的带宽优化策略,旨在提高图像传输效率与质量,降低带宽消耗。通过分析当前高清图像通信面临的问题,提出了包括选择合适的视频编码算法、应用数据压缩技术、采用动态码率调整技术、实施带宽分流与边缘计算以及采用双码流技术结合策略在内的多种优化方法。这些策略不仅能够有效减少数据传输量,而且能在有限带宽条件下保证高清图像的稳定传输,改善用户体验。

基于子块差特征的分形图像压缩编码技术

分形图像压缩编码一直以高压缩比而著称,由于其编码时间较长而饱受诟病,因此改进采用子块差特征的分形图像编码算法。分割后的图像块依照图像显著度分为重要区域块与非重要区域块,并将各类图像块以子块特征排序。设定显著度阈值,当小于设定的阈值,则认为是非重要区域,否则为重要区域;匹配搜索过程中,在同类别的值域块库中进行匹配搜索,搜索满足设定的误差阈值范围码本块。

基于深度学习的分形图像压缩编码模型设计

针对传统编码模型存在的图像分割处理效果不佳、分割精度不高、耗时较长以及编码质量较差的问题,提出基于深度学习的分形图像压缩编码模型。建立图像分割约束条件,对图像进行分割处理,以处理后的图像作为深度学习中ResNet网络模型的输入,提取原始分形图像的特征并将图像块分类;建立特征图像块匹配规则,排序图像块,记录分形码,将分形码作为图像在度量空间内的表现形式,通过度量空间的压缩变换实现分形图像压缩编码。实验结果表明:所提模型的图像压缩编码质量较高,具有一定的应用价值。

DATA COMPRESSION FOR CHINESE CHARACTERS USING TRANSFORM RUNLENGTH CODING

In the transmission of two-tone images, one-dimensional run-length coding (ODRLC) is one of the most commonly used approaches. T. S. Huang and others have found that the compression ratio of ODRLC can be improved by coding the black run-length (BRL) and the white run-length (WRL) separately. But

基于图像块叉迹的快速分形图像编码算法

分形图像编码能够在高压缩比下高质量地重构图像,但需要较长的编码时间.因此,迫切需要各种快速编码算法以扩大其应用领域.分形编码的时间主要花费于在一个海量码本中搜索每个输入子块的最佳匹配块.针对这个问题,该文提出一种快速分形编码算法,它基于图像块的一种新特征———叉迹,能够在较小的搜索范围内完成输入子块的最佳匹配.实验显示,该算法能够大大缩短编码时间,同时实现和全搜索分形编码算法相同或更好的图像质量.

数据压缩字典与快速图像分形映射压缩算法

针对分形编码压缩时间太长、以至于该算法很难达到实用的缺点 ,提出了对图像进行分级预处理 ,即根据被压缩图像的实际情况建立一个数据压缩字典 ,使得被压缩图像的匹配块仅在数据压缩字典指定的块中进行 ,从而大大减少图像压缩过程中的搜索范围和压缩时间·此外 ,给出了建立数据压缩字典的理论基础 ,并根据此思想给出了一个新的实现分形编码算法·实验表明 ,在提高压缩比的同时 。

基于平均偏差排序的快速分形图像编码

分形图像编码是一种很有前途的压缩技术 ,但由于其编码时间长、计算复杂性高 ,因而阻碍了它的广泛应用 ,针对此问题 ,提出了一种快速的分形编码算法。这种算法是首先将码本按照平均偏差大小进行排序 ,然后使用二分搜索法寻找给定 Range块在平均偏差意义下的最好匹配码块 ,进而利用一个联系均方根和平均偏差的不等式来在这个最好匹配码块的邻域中搜索 Range块在均方根意义下的最佳匹配码块。实验结果显示 ,在主观质量略有下降的条件下 ,该算法编码过程显著快于基本分形算法。

基于规范子块五点和的快速分形图像编码

分形图像编码通常需要较长的时间,编码时间主要花费于在一个海量码本中搜索每个输入子块的最佳匹配块.针对这个问题,提出一个限制搜索空间的算法.它主要基于图像块的一种新特征——五点和,把搜索范围限制在初始匹配块(五点和意义下与输入R块最接近的D块)的邻域内.实验表明:该算法能够大大减少子块匹配比较的数量,与基于叉迹的快速分形算法比较,在相同的搜索邻域内,在编码时间、图像质量和压缩比方面都更优.

图像压缩中的几种编码方法

首先讨论了几种经典的图像编码算法:行程编码压缩算法、哈夫曼编码压缩算法、LZW压缩算法及离散余弦变换,并给出了各自的压缩原理和特点,然后介绍了几种较新的图像编码方法,这些编码方法分别使用了分形技术、小波技术和人工神经网络技术,并且简要叙述了这三种技术的原理、算法步骤、优缺点,还对这些技术的发展进行了展望。