Image coding based on wavelet transform and uniform scalar dead zone quantizer

In this paper, a new wavelet transform image coding algorithm is presented. The discrete wavelet transform (DWT) is applied to the original image. The DWT coefficients are firstly quantized with a uniform scalar dead zone quantizer. Then the quantized coefficients are decomposed into four symbol streams: a binary significance map symbol stream, a binary sign stream, a position of the most significant bit (PMSB) symbol stream and a residual bit stream. An adaptive arithmetic coder with different context models is employed for the entropy coding of these symbol streams. Experimental results show that the compression performance of the proposed coding algorithm is competitive to other wavelet-based image coding algorithms reported in the literature.     

三维图像的任意形状ROI小波零块压缩编码算法

感兴趣区(ROI)编码是在JPEG2000中提出的一种重要的技术,然而JPEG2000算法却无法同时支持任意形状ROI和任意提升因子.本文提出了一种基于任意形状ROI和3D提升小波零块编码的3D体数据图像压缩算法.新的算法支持ROI内外从有损到无损的编码.一种简单的任意形状无损ROI掩码(Mask)生成方法被提出.考虑到3D子带的特点,我们采用改进的3DSPECK零块算法对变换后的系数进行编码.一些其它支持任意形状ROI编码的算法也在本文中被评估,试验显示本文算法具有更好的编码性能.     

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.     

Human visual weighted progressive image transmission

A progressive image transmission scheme which combines transform coding with the human visual system (HVS) model is developed. The adaptive transform coding of W.H. Chen and C.H. Smith (1977) is utilized to classify an image into four equally populated subblocks based on their AC energies. The modulation transfer function (MTF) of the HVS model is obtained experimentally, based on processing a number of test images. A simplified technique for incorporating the MTF into the discrete cosine transform (DCT) domain is utilized. In the hierarchical image buildup, the image is first reconstructed from the DC coefficients of all subblocks. Further transmission hierarchy of transform coefficients and consequent image buildup are dependent on their HVS weighted variances. The HVS weighted reconstructed images are compared to the ones without any weighting at several stages. The HVS weighted progressive image transmission results in perceptually higher quality images compared to the unweighted scheme     

结合视觉感知的对象基嵌入式图像编码中的码率控制方法

结合人眼视觉感知特性,提出一种面向对象基嵌入式编码的码率控制算法.首先结合人眼视觉感知特性估计各对象的重要性并确定其编码优先级;然后依据优先级对视觉对象进行位平面建模和熵编码,得到各自独立的码流;最后基于率失真优化理论在给定码率下对对象码流进行优化截取和重装.仿真实验结果表明,所提算法可以对不同重要对象进行差异化编码和传输,与基于PCRD算法的码率控制方法相比,新算法能提高重构图像的整体视觉效果     

小波图像的膨胀-游程编码算法

提出了一种基于形态膨胀运算和游程编码的新型小波编码器膨胀-游程(Dilation-Run)算法。编码器根据图像小波变换后重要系数的带内聚类特性和重要系数分布的带间相似性,利用数学形态学中的膨胀运算搜索并编码各聚类中的重要系数;同时使用一种高效的游程编码技术对各聚类的种子系数,即膨胀运算起始点的位置进行编码,从而避免了小波图像中非重要系数的逐个编码。编码器算法简单,并且基于位平面实现,因此输出码流具有渐进性。实验结果表明,膨胀-游程算法的性能优于零树小波编码器SPIHT,并能与两种形态学小波编码器MRWD 和SLCCA的性能媲美。对于聚类特性显著的图像,算法的性能则优于上述形态学小波编码器。     

基于多描述和不等差错保护的航空遥感图像传输方法

 本文提出了一种针对航空遥感图像传输的信源信道联合编码方法.将小波变换后的图像进行小波树分组以形成多描述,并重复描述重要的低频子带系数;然后利用改进的多级树集合分裂(SPIHT)算法对每个描述单独编码,并为其提供不等差错保护.为保证编码实时性,提出了一种快速的码率分配搜索算法.仿真结果表明该方法在频率选择性莱斯衰落信道下实现了遥感图像的鲁棒传输,且具有较低的复杂度.     

一种基于小波域图像纹理特征的数字水印技术

提出一种基于小波域图像纹理特征的彩色图像盲水印算法。该算法根据。图像小波变换后小波系数的特点选择视觉重要系数，量化嵌入二值水印序列。利用人类视觉系统的纹理掩蔽特性和照度掩蔽特性自适应调整量化间距，以保证水印的透明性。算法实现了水印的盲检测。仿真实验的结果表明，算法对各种常用的水印攻击方法具有较好的鲁棒性。     

Edge-based motion compensated classified DCT with quadtree for image sequence coding

The motion compensated discrete cosine transform coding (MCDCT) is an efficient image sequence coding technique. In order to further reduce the bit-rate for the quantizied DCT coefficients and keep the visual quality, we propose an adaptive edge-based quadtree motion compensated discrete cosine transform coding (EQDCT). In our proposed algorithm, the overhead moving information is encoded by a quadtree structure and the nonedge blocks will be encoded at lower bit-rate but the edge blocks will be encoded at higher bit-rate. The edge blocks will be further classified into four different classes according to the orientations and locations of the edges. Each class of edge blocks selects the different set of the DCT coefficients to be encoded. By this method, we can just preserve and encode a few DCT coefficients, but still maintain the visual quality of the images. In the proposed EQDCT image sequence coding scheme, the average bit-rate of each frame is reduced to 0.072 bit/pixel and the average PSNR value is 32.11 dB.     

基于小波变换的文本不可见水印算法

文本数字水印是确保文本数字产品著作权人的合法利益,避免非法盗版的一种有效技术手段。该文依据文本图像的纹理特点,结合人类视觉系统掩蔽特性,提出了一种基于小波变换的文本不可见水印算法。算法首先通过将载体文本图像一级小波变换,然后提取出变换后的对角细节子图,最后随机选择子图中系数模值较大的点嵌入水印。实验结果显示,该算法具有良好的不可见性性和鲁棒性,可以抵抗各种常见的攻击。     

基于人眼对比敏感视觉特性的彩色图像盲水印算法

为了解决水印透明性、鲁棒性与水印容量之间的平衡问题,结合人眼对比敏感度视觉特性,提出了一种彩色图像的盲水印算法。算法首先结合人眼对比敏感度视觉特性及其模型,在频域中计算人眼感知原始图像的对比敏感度阈值;其次结合人眼对强度的感知模型,计算水印图像信息的强度感知值;然后结合嵌入强度系数值,以感知原始图像的对比敏感度阈值为水印嵌入量的门限,实现数字水印的嵌入;最后依据密钥,采用逆过程提取水印。实验结果表明,在压缩质量因子为20%、中间剪切1/4区域的较强攻击下,提取水印与原水印的相似度达到0.78以上,且保证了较好的水印透明性。     

基于DWT SVD域的彩色图像水印算法

针对数字产品的版权保护问题,提出了一种结合整数小波变换（DWT）和矩阵奇异值分解（SVD）的彩色图像非盲水印算法。该算法首先对待处理图像的蓝色分量进行整数小波分解,再将得到的低频子带的小波系数矩阵进行奇异值分解,然后对水印灰度图像进行奇异值分解,水印图像的奇异值被嵌入到由源图像小波系数分解得到的奇异值中。经过奇异值分解逆变换及整数小波逆变换得到含水印图像。实验结果表明,该算法对加噪、滤波、JPEG压缩、剪切、缩放和旋转攻击均有较强的抵抗力,具有较好的实用价值。     

基于相干叠加与模均等矢量分解的光学图像加密算法

为了解决光学图像加密技术将密文相位信息主要集中在纯相位掩码中,使其存在轮廓显现问题,该文提出相干叠加与模均等矢量分解的光学图像加密算法。首先,对输入图像进行归一化处理;基于映射,利用输入图像的像素特性,生成其初值,通过迭代映射,输出混沌相位掩码;借助相位掩码,对图像完成调制,并结合Fourier变换,对调制图像进行处理,输出其Fourier频谱;再对该频谱分别进行等模分解,获得两个掩码;再基于不同分数阶的Fourier机制,对两个掩码进行变换;最后,引入相位-幅度截断编码技术,设计单向编码机制,通过激光束的相干叠加,输出编码密文的幅度与相位信息,将相位部分视为编码密文,把幅度信息作为解密密钥。通过等模分解技术,将输入明文演变为4个不同的相位与幅度信息,有效解决了轮廓显现问题。实验结果显示:与当前基于干涉原理的图像加密机制相比,所提算法的安全性更高,有效消除了轮廓显现问题。     

一种基于HVS的图像易碎水印

作为多媒体认证和篡改检测的一种新技术,易碎水印正得到越来越多的关注.基于小波变换,结合量化调制过程,本文提出了一个易碎水印算法.该算法具有以下特点:(1)构造水印金字塔,便于实现水印的多分辨率检测;(2)结合视觉特性量化调制,尽可能减小视觉失真;(3)结合图像融合技术的多分辨率检测,使检测结果更准确;(4)抵抗一定程度的JPEG有损压缩.为了实现篡改的检测,本文给出了一个攻击判别方案,以区分恶意攻击和偶然攻击.实验表明,嵌入的水印在脆弱性和鲁棒性上达到了较好的统一,能够抵抗一定程度的压缩而不影响篡改检测的精确性.     

Multi-focus image fusion with Geometrical Sparse Representation

Multi-focus image fusion aims to generate an image with all objects in focus by integrating multiple partially focused images. It is challenging to find an effective focus measure to evaluate the clarity of source images. In this paper, a novel multi-focus image fusion algorithm based on Geometrical Sparse Representation (GSR) over single images is proposed. The main novelty of this work is that it shows the potential of GSR coefficients used for image fusion. Unlike the traditional sparse representation-based (SR) methods, the proposed algorithm does not need to train an overcomplete dictionary and vectorize the signal. In our algorithm, using a single dictionary image, the source images are first represented by geometrical sparse coefficients. Specifically, we employ a weighted GSR model in the sparse coding phase, ensuring the importance of the center pixel. Then, the weighted GSR coefficient is used to measure the activity level of the source image and an average pooling strategy is applied to obtain an initial decision map. Third, the decision map is refined with a simple post-processing. Finally, the fused all-in-focus image is constructed with the refined decision map. Experimental results demonstrate that the proposed method can be competitive with or even superior to the state-of-the-art fusion methods in both subjective and objective comparisons.     

Foveation scalable video coding with automatic fixation selection

Image and video coding is an optimization problem. A successful image and video coding algorithm delivers a good tradeoff between visual quality and other coding performance measures, such as compression, complexity, scalability, robustness, and security. In this paper, we follow two recent trends in image and video coding research. One is to incorporate human visual system (HVS) models to improve the current state-of-the-art of image and video coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate scalable image and video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. Specifically, we propose a foveation scalable video coding (FSVC) algorithm which supplies good quality-compression performance as well as effective rate scalability. The key idea is to organize the encoded bitstream to provide the best decoded video at an arbitrary bit rate in terms of foveated visual quality measurement. A foveation-based HVS model plays an important role in the algorithm. The algorithm is adaptable to different applications, such as knowledge-based video coding and video communications over time-varying, multiuser and interactive networks.     

Zerotree pattern coding of motion picture residues forerror-resilient transmission of video sequences

This paper describes a compression scheme for difference-image residues in video coding. Structured spatial patterns are used to map residue pixel values into a quadtree structure, which is then coded in significance order with the SPIHT algorithm. Thus the wavelet coefficient values of standard zerotree coding are replaced by untransformed (but carefully positioned) residue pixel values. The new zerotree pattern coding method compresses as well as zerotree wavelet coding and much better than DCT coding (as in MPEG) over error-free channels. Over noisy channels, zerotree pattern coding provides built-in error resilience, allowing transmission of residue data without error control overhead. A simple postprocessing technique provides additional error concealment     

Efficient significance map coding using block-based zerotree andquadtree

For wavelet-based image coding, it is important to efficiently code the significance map for each bit plane. The proposed algorithm provides an effective method for coding the significance map by using block-based zerotree and quadtree decomposition, and demonstrates good R-D performance especially at low bit rates and for images with rich texture     

X树非平衡保护多描述编码

本文提出了一种新的多描述图像编码方案X树非平衡保护多描述子带编码。首先把小波变换图像分解为结构信息和残余非零系数。其中结构信息包括非零系数的位置、符号和位数,是小波变换图像的关键信息,必须复制到每个描述。残余非零系数则通过多描述标量量化器生成两个索引分别放入不同的描述。两个描述通过不同的信道进行传输。由于每个描述都包含了图像的重要信息,因此,接收到一个信道的信息就可以重建出一定质量的图像。如果所有的信道传输的信息都被接收,就会得到比任何一个单独信道更好的重建图像。实验结果验证了本算法的有效性和正确性。     

Fusion algorithm for multisensor images based on discrete multiwavelet transform

The authors review the notion of multiwavelets and describe the use of the discrete multiwavelet transform (DMWT) in image fusion processing. Multiwavelets are extensions from scalar wavelets, and have several advantages in comparison with scalar wavelets. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. A novel fusion algorithm is presented for multisensor images based on the discrete multiwavelet transform that can be performed at pixel level. After the registering of source images, a pyramid for each source image can be obtained by applying decomposition with multiwavelets in each level. The multiwavelet decomposition coefficients of the input images are appropriately merged and a new fused image is obtained by reconstructing the fused multiwavelet coefficients. This image fusion algorithm may be used to combine images from multisensors to obtain a single composite with extended information content. The results of experiments indicate that this image fusion algorithm can provide a more satisfactory fusion outcome.