Efficient Quadtree based Fractal Image Coding Scheme in Wavelet Transform Domain *

This paper presents an efficient quadtree based fractal image coding scheme in wavelet transform domain based on the wavelet based theory of fractal image compression introduced by Davis. In the scheme, zerotrees of wavelet coefficients are used to reduce the number of domain blocks, which leads to lower bit cost required to represent the location information of fractal coding, and overall entropy constrained optimization is performed for the decision trees as well as for the sets of scalar quantizers and self quantizers of wavelet subtrees. Experiment results show that at the low bit rates, the proposed scheme gives about 1 dB improvement in PSNR over the reported results.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Second Generation Wavelet Applied to Lossless Compression Coding of Image

In this paper, the second generation wavelet transform is applied to image lossless coding, according to its characteristic of reversible integer wavelet transform. The second generation wavelet transform can provide higher compression ratio than Huffman coding while it reconstructs image without loss compared with the first generation wavelet transform. The experimental results show that the se cond generation wavelet transform can obtain excellent performance in medical image compression coding.

VLSI Implementation of a Wavelet Image Coder

A modular architecture for two dimension (2 D) discrete wavelet transform (DWT) is designed.The image data can be wavelet transformed in real time,and the structure can be easily scaled up to higher levels of DWT.A fast zerotree image coding (FZIC) algorithm is proposed by using a simple sequential scan order and two flag maps.The VLSI structure for FZIC is then presented.By combining 2 D DWT and FZIC,a wavelet image coder is finally designed.The coder is programmed,simulated,synthesized,and successfully verified by ALTERA CPLD.

An Adaptive Method of Image CompressionBased on Wavelet Analysis

Through research for image compression based on wavelet analysis in recent years, we put forward an adaptive wavelet decomposition strategy. Whether sub-images are to be decomposed or not are decided by their energy defined by certain criterion. Then we derive the adaptive wavelet decomposition tree (AWDT) and the way of adjustable compression ratio. According to the feature of AWDT, this paper also deals with the strategies which are used to handle different sub-images in the procedure of quantification and coding of the wavelet coefficients. Through experiments, not only the algorithm in the paper can adapt to various images, but also the quality of recovered image is improved though compression ratio is higher and adjustable. When their compression ratios are near, the quality of subjective vision and PSNR of the algorithm are better than those of JPEG algorithm.

Blind and readable image watermarking using wavelet tree quantization

A blind and readable image watermarking scheme using wavelet tree quantization is proposed. In order to increase the algorithm robustness and ensure the watermark integrity,error correction coding techniques are used to encode the embedded watermark. In the watermark embedding process, the wavelet coefficients of the host image are grouped into wavelet trees and each watermark bit is embedded by using two trees. The trees are so quantized that they exhibit a large enough statistical difference, which will later be used for watermark extraction. The experimental results show that the proposed algorithm is effective and robust to common image processing operations and some geometric operations such as JPEG compression, JPEG2000 compression, filtering, Gaussian noise attack, and row-column removal. It is demonstrated that the proposed technique is practical.

Image Coding Using Wavelet Transform and EntropyConstrained Vector Quantization with Quadtree Structure Vectors

This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with

Three-dimensional quantum wavelet transforms

Wavelet transform is being widely used in the field of information processing.One-dimension and two-dimension quantum wavelet transforms have been investigated as important tool algorithms.However,three-dimensional quantum wavelet transforms have not been reported.This paper proposes a multi-level three-dimensional quantum wavelet transform theory to implement the wavelet transform for quantum videos.Then,we construct the iterative formulas for the multi-level three-dimensional Haar and Daubechies D4 quantum wavelet transforms,respectively.Next,we design quantum circuits of the two wavelet transforms using iterative methods.Complexity analysis shows that the proposed wavelet transforms offer exponential speed-up over their classical counterparts.Finally,the proposed quantum wavelet transforms are selected to realize quantum video compression as a primary application.Simulation results reveal that the proposed wavelet transforms have better compression performance for quantum videos than two-dimension quantum wavelet transforms.

Medical image lossless compression based on combining an integer wavelet transform with DPCM

To improve the classical lossless compression of low efficiency,a method of image lossless compression with high efficiency is presented.Its theory and the algorithm implementation are introduced.The basic approach of medical image lossless compression is then briefly described.After analyzing and implementing differential plus code modulation(DPCM)in lossless compression,a new method of combining an integer wavelet transform with DPCM to compress medical images is discussed.The analysis and simulation results show that this new method is simpler and useful.Moreover,it has high compression ratio in medical image lossless compression.

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.

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional （1D） linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional （2D） integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ （WSFCVQ）, the method of improved BiBlock zero tree coding （IBBZTC） and the method of feature spectral vector quantization （FSVQ）, the peak signal-to-noise ratio （PSNR） of this method can enhance over 9 dB, and the total compression performance is improved greatly.

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.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

Lossy-to-Lossless Compression of Hyperspectral Image Using the 3D Set Partitioned Embedded ZeroBlock Coding Algorithm

In this paper, we propose a three-dimensional Set Partitioned Embedded ZeroBlock Coding (3D SPEZBC) lossy-to-lossless compression algorithm for hyperspectral image which is an improved three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm. The algorithm adopts the 3D integer wavelet packet transform proposed by Xiong et al. to decorrelate, the set-based partitioning zeroblock coding to process bitplane coding and the con-text-based adaptive arithmetic coding for further entropy coding. The theoretical analysis and experimental results demonstrate that 3D SPEZBC not only provides the same excellent compression performances as 3D EZBC, but also reduces the memory requirement compared with 3D EZBC. For achieving good coding performance, the diverse wave-let filters and unitary scaling factors are compared and evaluated, and the best choices were given. In comparison with several state-of-the-art wavelet coding algorithms, the proposed algorithm provides better compression performance and unsupervised classification accuracy.

基于长度可变密钥的光学图像加密方法

为提升光学图像加密效果,设计了一种基于长度可变密钥的光学图像加密方法。基于混沌方程构成混沌序列均匀化普适算法,运用该算法生成均匀化伪随机序列,以此作为长度可变密钥,经空间光调制器调制后,转换此类密钥为伪随机相位掩模(PRPM),运用两个转换后PRPM双重随机相位编码初始光学图像,并结合变形分数傅里叶变换(AFrFT),相位转换编码后光学图像,获得加密光学图像,完成光学图像加密。实验结果显示,该方法可针对不同光学图像生成敏感性与唯一性密钥,实现不同光学图像的加密处理,加密效果显著,加密光学图像的像素频数分布均匀,且解密后光学图像与初始光学图像几乎一致,整体加解密效果理想,可有效保障光学图像的安全性,而且本方法可以在最短3 s的时间内完成光学图像的加密和解密,加解密效率更高,综合效果更佳。

基于QR码与级联Fourier变换的图像光学加密算法

为实现对多幅图像完成同步加密,并提高密文的安全性与抗几何变换攻击能力,提出基于快速响应码(QR)与级联分数阶Fourier变换的多图像光学加密算法。借助Logistic映射,获取随机掩码,联合Fresnel波带、Hilbert模型,构建调制掩码;引入复数理论,借助模板图像对第一幅明文实施处理,形成复数图像;采用分数阶Fourier变换,借助调制掩码,对复数图像实施处理,并基于相位-幅度截断机制,得到相位分布与密钥;再结合相位截断方法与分数阶Fourier变换,设计级联快速傅里叶变换(FFT)加密机制,通过调制掩码与相位分布,对剩余的多幅明文实施频域变换处理,输出融合幅度图像,并将其转换为对应的QR码;引入Gyrator变换,通过相应的光电装置,利用激光束对QR码完成干涉处理,得到密文。测试数据表明:相对已有的多图像光学加密技术,本算法具备更高的保密安全性与抗攻击能力,在裁剪变换下,其复原图像的失真度更小。

GLOBAL MEASURE ON IMAGE CONTENT

This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.

基于双频域特征聚合的低照度图像增强

针对低照度图像质量较差、噪声多、纹理模糊等问题,提出一种基于双频域特征聚合的低照度增强网络(dual frequency-domain feature aggregation network,DF-DFANet)。首先,构建频谱光照估计模块(frequency domain illumination estimation module,FDIEM)实现跨域特征提取,通过共轭对称约束调整频域特征图抑制噪声信号,并采用逐层融合方式提高多尺度融合效率以扩大特征图感受野范围。其次,设计多谱双注意力模块(multiple spectral attention module,MSAM)聚焦图像局部频率特征,通过小波域空间、通道注意力机制关注图像细节信息。最后,提出双域特征聚合模块(dual domain feature aggregation module,DDFAM)融合傅里叶域和小波域特征信息,利用激活函数计算自适应调整权重实现像素级图像增强,并结合傅里叶域全局信息提高融合效果。实验结果表明,在LOL数据集上所提网络的PSNR达到24.3714,SSIM达到0.8937。与对比网络相比,所提网络增强效果更具自然性。

小波变换下基于分形编码和Zernike矩的织物图像检索算法

为帮助纺织企业的工作人员快速、准确地从数据库中检索出与织物图像相同或相似的图像,提出了一种小波变换下基于分形编码和Zernike矩的织物图像检索算法。首先,利用小波变换获得低频分量,对变换后的低频子图进行分形编码,得到编码参数。然后,计算低频子图像的Zernike矩。将小波变换下的分形编码参数和Zernike矩相结合作为织物图像检索的特征量。相比于单特征检索方法,该算法克服了精度低、耗时长的问题。与基本分形算法(BFIC)、联合正交分形参数和改进的Hu不变矩算法(HVKF)以及稀疏分形图像压缩算法(SFIC)相比,该算法确保了重建图像的质量和较低的编码时间。实验结果表明,织物图像检索的平均精度和平均召回率均高于现有的检索方法。

基于视频的人体心率检测算法研究

传统的心率检测方法有侵入式和非侵入接触式两种测量方法,但是会造成不便。论文提出一种基于IPPG(Image photoplethysmography)技术的非接触式人体心率测量方法,从人体的裸露皮肤提取脉搏波信号,在脉搏波信号中提取人体的心率值。通过摄像头采集人体面部的信息,提取感兴趣区域的灰度均值后,经处理得到人体的心率。论文还通过实验调整了心率检测系统中的感兴趣区域、采样点数和图像颜色格式,使检测精度得到提升。利用论文方法测量人体心率,与指夹式血氧仪的测量数据进行比对,结果的平均绝对误差为1.91次/min和均方根误差为2.15次/min。