Decoding algorithm for fractal image compression

A new iterative decoding method is proposed for fractal image compression. Convergence properties are provided. Experimental results show the superiority of the new method over the conventional decoding procedure     

一种改进的分形图像压缩算法

为了缩短分形编码时间,通过对图像定义域块和值域块的统计特性分析,提出了改进的分形压缩算法。设计最优匹配定义域块自适应搜索方法,缩短搜索范围;用值域块均值代替灰度偏移量,减少计算量。实验证明,运用这种改进的分形图像压缩算法进行图像压缩,在保持较高的解压图像质量的情况下,大幅缩短图像压缩编码时间。     

A fast encoding algorithm for fractal image compression using theDCT inner product

In this paper, a fast encoding algorithm is developed for fractal image compression. At each search entry in the domain pool, the mean square error (MSE) calculations of the given range block and the eight dihedral symmetries of the domain block are obtained simultaneously in the frequency domain, in which the redundant computations are all eliminated in the new encoding algorithm. It is shown in software simulation that the encoding time is about six times faster than that of the baseline method with almost the same PSNR for the retrieved image. The fast algorithm is performed to deal with the eight dihedral symmetries at each search entry. Therefore, it can be applied to various enhanced algorithms which are equipped with quadtree, classification, and other mechanisms.     

Region-based fractal image compression using heuristic search

Presents work carried out on fractal (or attractor) image compression. The approach relies on the assumption that image redundancy can be efficiently exploited through self-transformability. The algorithms described utilize a novel region-based partition of the image that greatly increases the compression ratios achieved over traditional block-based partitionings. Due to the large search spaces involved, heuristic algorithms are used to construct these region-based transformations. Results for three different heuristic algorithms are given. The results show that the region-based system achieves almost double the compression ratio of the simple block-based system at a similar decompressed image quality. For the Lena image, compression ratios of 41:1 can be achieved at a PSNR of 26.56 dB.     

Genetic algorithm based on discrete wavelet transformation for fractal image compression

In this paper, a genetic algorithm (GA) based on discrete wavelet transformation (DWT) is proposed to overcome the drawback of the time-consuming for the fractal encoder. First, for each range block, two wavelet coefficients are used to find the fittest Dihedral block of the domain block. The similar match is done only with the fittest block to save seven eighths redundant MSE computations. Second, embedding the DWT into the GA, a GA based on DWT is built to fast evolutionary speed further and maintain good retrieved quality. Experiments show that, under the same number of MSE computations, the PSNR of the proposed GA method is reduced 0.29 to 0.47 dB in comparison with the SGA method. Moreover, at the encoding time, the proposed GA method is 100 times faster than the full search method, while the penalty of retrieved image quality is relatively acceptable.     

Fast fractal image compression using the Hadamard transform

A new algorithm for fractal image compression is developed to speed up the encoder. This new method converts image blocks into frequency-like domains using the Hadamard transform, in which the computations of the best matched are performed. At each search entry, the best mean square error computations of the eight dihedral symmetries are reformulated into the form of inner products. By a precise derivation, all redundant computations are completely avoided. With this improved technique, the complexity of the encoder is substantially reduced. A simulation shows that, with the same PSNR and compression ratio, the new method requires less computation time than the baseline method     

Region-based fractal image compression

A fractal coder partitions an image into blocks that are coded via self-references to other parts of the image itself. We present a fractal coder that derives highly image-adaptive partitions and corresponding fractal codes in a time-efficient manner using a region-merging approach. The proposed merging strategy leads to improved rate-distortion performance compared to previously reported pure fractal coders, and it is faster than other state-of-the-art fractal coding methods.     

Progressive decoding method for fractal image compression

Fractal image compression is an efficient technique for compactly coding images, in which an image is encoded by a contractive transformation whose fixed point is close to the original image, and then is decoded by using an iteration procedure stemmed from the well known Banach fixed-point theorem. A new fixed-point iteration theorem with a control parameter is presented, which provides a novel iteration procedure that progressively approaches the fixed point of a contractive transformation and particularly reverts back to the conventional iteration procedure when the control parameter is set as one. Based on the new iteration procedure, a progressive decoding algorithm is proposed for fractal image compression, which does not need any specific fractal encoder and is useful for low bandwidth transmission. The experimental results demonstrate that the progressive fractal decoding is capable of controlling the decoding iteration procedure by varying the control parameter values and displaying progressively how the original image is obtained from a black image or another image at each step of the increasing iterations.     

分形在视频图像压缩中的应用

阐述了分形图像压缩的理论基础,了分形在视频图像空中的应用。分形图像压缩编码方法能够实现很高的压缩比,具有很高的实用价值。     

Modified VQ-BTC algorithm for image compression

VQ-BTC is a recent technique used in the coding of image data to combat edge degradation produced by vector quantisation (VQ) or block truncation coding (BTC). However, it has high encoding complexity and needs a large amount of memory to store 31 codebooks at both the encoder and decoder. A modified VQ-BTC (MVQ-BTC) algorithm is presented which achieves a performance close to that of VQ-BTC, but needs only three codebooks, and requires less computation time than VQ-BTC     

Combining fractal image compression and vector quantization

In fractal image compression, the code is an efficient binary representation of a contractive mapping whose unique fixed point approximates the original image. The mapping is typically composed of affine transformations, each approximating a block of the image by another block (called domain block) selected from the same image. The search for a suitable domain block is time-consuming. Moreover, the rate distortion performance of most fractal image coders is not satisfactory. We show how a few fixed vectors designed from a set of training images by a clustering algorithm accelerates the search for the domain blocks and improves both the rate-distortion performance and the decoding speed of a pure fractal coder, when they are used as a supplementary vector quantization codebook. We implemented two quadtree-based schemes: a fast top-down heuristic technique and one optimized with a Lagrange multiplier method. For the 8 bits per pixel (bpp) luminance part of the 512kappa512 Lena image, our best scheme achieved a peak-signal-to-noise ratio of 32.50 dB at 0.25 bpp.     

A wavelet-based analysis of fractal image compression

Why does fractal image compression work? What is the implicit image model underlying fractal block coding? How can we characterize the types of images for which fractal block coders will work well? These are the central issues we address. We introduce a new wavelet-based framework for analyzing block-based fractal compression schemes. Within this framework we are able to draw upon insights from the well-established transform coder paradigm in order to address the issue of why fractal block coders work. We show that fractal block coders of the form introduced by Jacquin (1992) are Haar wavelet subtree quantization schemes. We examine a generalization of the schemes to smooth wavelets with additional vanishing moments. The performance of our generalized coder is comparable to the best results in the literature for a Jacquin-style coding scheme. Our wavelet framework gives new insight into the convergence properties of fractal block coders, and it leads us to develop an unconditionally convergent scheme with a fast decoding algorithm. Our experiments with this new algorithm indicate that fractal coders derive much of their effectiveness from their ability to efficiently represent wavelet zero trees. Finally, our framework reveals some of the fundamental limitations of current fractal compression schemes.     

视觉传感器网络中基于散度模型的协作式图像压缩机制

结合视觉传感器网络的协同工作特性,提出一种基于散度模型的图像压缩机制.理论分析和实验结果表明,该压缩机制不仅可以减少图像数据量,而且由于压缩后每字节数据所含信息量由各簇内节点的二值量化像素均分,不会引起传输错误在图像中大面积扩散.相比于采用传统的图像压缩算法,随着平均分组丢失率的增高,接收图像峰值信噪比较高.     

红外图像差分盒维分形算法的FPGA实现

差分盒维分形算法作为一种红外图像分形维数提取算法广泛应用于红外图像目标检测跟踪算法中.针对其计算量大,单DSP无法单帧实时实现的缺陷,利用FPGA并行流水特点,采用模块化设计思想设计并实现了差分盒维分形算法,算法主要由四点极值计算模块和使能控制模块及对数计算模块组成.四点极值计算模块计算图像分割网格像素灰度极值,使能控制模块控制网格内像素灰度极值的计算开始时刻,而对数计算模块计算每一尺度下的分形维.仿真结果表明:采用FPGA实现差分盒维分形算法处理延时短,最高系统主频可达126 MHz,满足实时处理的要求.     

Fractal image compression based on spatial correlation and hybrid genetic algorithm

In order to solve the high complexity of the conventional encoding scheme for fractal image compression, a spatial correlation hybrid genetic algorithm based on the characteristics of fractal and partitioned iterated function system (PIFS) is proposed in this paper. There are two stages for the algorithm: (1) Make use of spatial correlation in images for both range and domain pool to exploit local optima. (2) Adopt simulated annealing genetic algorithm (SAGA) to explore the global optima if the local optima are not satisfied. In order to avoid premature convergence, the algorithm adopt dyadic mutation operator to take place of the traditional one. Experiment results show that the algorithm convergent rapidly. At the premise of good quality of the reconstructed image, the algorithm saved the encoding time and obtained high compression ratio.     

A lossless image compression algorithm using variable block sizesegmentation

The redundancy in digital image representation can be classified into two categories: local and global. In this paper, we present an analysis of two image characteristics that give rise to local and global redundancy in image representation. Based on this study, we propose a lossless image compression scheme that exploits redundancy both at local and global levels in order to obtain maximum compression efficiency. The proposed algorithm segments the image into variable size blocks and encodes them depending on the characteristics exhibited by the pixels within the block. The proposed algorithm is implemented in software and its performance is better than other lossless compression schemes such as the Huffman, the arithmetic, the Lempel-Ziv and the JPEG.     

A singular-value decomposition-based image watermarking using genetic algorithm

In this paper, a novel optimal watermarking scheme based on singular-value decomposition (SVD) using genetic algorithm (GA) is presented. The singular values (SVs) of the host image are modified by multiple scaling factors to embed the watermark image. Modifications are optimised using GA to obtain the highest possible robustness without losing the transparency. Experimental results show both the significant improvement in transparency and the robustness under attacks.     

Statistical feature extraction based technique for fast fractal image compression

Fractal image compression is an innovative way of image representation by using relationships among the sub-section of image itself. It utilizes the existence of self-symmetry and uses affine contractive transforms. This technique has manifold advantages like, very high compression ratio, high decompression speed, high bit-rate and resolution independence, but high computation time expenses of suitable domain search in coding phase is the major bottleneck of the technique. This paper presents a fast fractal compression scheme based on feature extraction and innovative way of image comparison. In proposed development the complexity of suitable domain search is reduced by transforming the problem from image domain to vector domain. Simulation results confirms that suggested variant leads to a faster system as compared to existing state-of art Fractal Image Compression techniques.     

高速采集图像实时压缩算法

为了解决高速图像采集给图像数据的实时传输和存储带来的困难,提出了一种基于提升形态Haar小波的面向自动目标识别(ATR)的图像压缩算法——目标形态小波图像编码(TMWC).该算法将形态Haar小波与数学形态学方法结合应用.首先将采集图像进行二维提升形态Haar小波分解,结合目标感兴趣区域(ROI)检测要求的特点,仅在尺...     

海洋合成孔径雷达图像的一种高效压缩方法

针对通用压缩算法未利用合成孔径雷达（SAR）图像特征的不足,提出一种基于概率分布的自适应海洋SAR图像压缩算法。利用海洋SAR图像的概率分布,根据目标的分布设计量化方案,使目标和背景得到不同程度的保留。利用场景的稀疏性,将阈值以上的像素映射到三元组,对其灰度和位置信息分别熵编码;利用剩余背景层灰度偏差较小的特点作位平面编码。实验结果表明,该算法能有效地压缩图像,同码率下峰值信噪比（PSNR）较JPEG2000高5dB～10dB。本文算法复杂度低,对比度保持好,适用于针对不同需求的海面舰船SAR图像压缩。