Recurrent pattern matching based stereo image coding using linear predictors

The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based generic image encoding solution which has been investigated earlier for the compression of stereo images with successful results. While first MMP-based proposals for stereo image coding employed dictionary-based techniques for disparity compensation, posterior developments have demonstrated the advantage of using predictive methods. In this paper, we focus on recent investigations on the use of predictive methods in the MMP algorithm and propose a new prediction framework for efficient stereo image coding. This framework comprises an advanced intra directional prediction model and a new linear predictive scheme for efficient disparity compensation. The linear prediction model is the main novelty of this work, combining adaptive linear models estimated by least-squares algorithm with fixed linear models provided by the block-matching algorithm. The performance of the proposed intra prediction and disparity compensation methods when applied in an MMP encoder has been evaluated experimentally. Comparisons with the current stereo image coding standards showed that the proposed MMP algorithm significantly outperforms the Stereo High Profile of H.264/AVC standard. In addition, it presents a competitive performance relative to the MV-HEVC standard. These results also suggest that current stereo image coding standards may benefit from the proposed linear prediction scheme for disparity compensation, as an extension to the omnipresent block-matching solution.     

Transform coding of stereo image residuals

Stereo image compression is of growing interest because of new display technologies and the needs of telepresence systems. Compared to monoscopic image compression, stereo image compression has received much less attention. A variety of algorithms have appeared in the literature that make use of the cross-view redundancy in the stereo pair. Many of these use the framework of disparity-compensated residual coding, but concentrate on the disparity compensation process rather than the post compensation coding process. This paper studies specialized coding methods for the residual image produced by disparity compensation. The algorithms make use of theoretically expected and experimentally observed characteristics of the disparity-compensated stereo residual to select transforms and quantization methods. Performance is evaluated on mean squared error (MSE) and a stereo-unique metric based on image registration. Exploiting the directional characteristics in a discrete cosine transform (DCT) framework provides its best performance below 0.75 b/pixel for 8-b gray-scale imagery and below 2 b/pixel for 24-b color imagery, In the wavelet algorithm, roughly a 50% reduction in bit rate is possible by encoding only the vertical channel, where much of the stereo information is contained. The proposed algorithms do not incur substantial computational burden beyond that needed for any disparity-compensated residual algorithm.     

A hybrid scheme for low bit-rate coding of stereo images

We propose a hybrid scheme to implement an object driven, block based algorithm to achieve low bit-rate compression of stereo image pairs. The algorithm effectively combines the simplicity and adaptability of the existing block based stereo image compression techniques with an edge/contour based object extraction technique to determine appropriate compression strategy for various areas of the right image. Unlike the existing object-based coding such as MPEG-4 developed in the video compression community, the proposed scheme does not require any additional shape coding. Instead, the arbitrary shape is reconstructed by the matching object inside the left frame, which has been encoded by standard JPEG algorithm and hence made available at the decoding end for those shapes in right frames. Yet the shape reconstruction for right objects incurs no distortion due to the unique correlation between left and right frames inside stereo image pairs and the nature of the proposed hybrid scheme. Extensive experiments carried out support that significant improvements of up to 20% in compression ratios are achieved by the proposed algorithm in comparison with the existing block-based technique, while the reconstructed image quality is maintained at a competitive level in terms of both PSNR values and visual inspections.     

一种结合人眼对比度感知特性的彩色图像压缩算法

为了使图像压缩后的效果更加符合人眼感知特性,提出了一种结合人眼对比度敏感视觉特性的图像压缩算法。算法首先结合视觉特性和图像变换域频谱系数特征,提出一种图像的角频率的计算方法,并依据计算的角频率提出一种人眼觉察图像最小误差阈值的计算方法;然后以此阈值作为量化步长,提出一种图像变换域频谱系数的量化方法;最后采用霍夫曼编码算法进行编解码,实现图像的压缩。并对三幅彩色图像进行了仿真实验,结果表明:与JPEG技术相比,三幅彩色图和各分量图的平均压缩比、PSNR和SSIM依次提高了10.4807%、6.9879%和2.6494%。表明提出的结合人眼视觉特性的图像压缩算法是一种较好的、有实用价值的压缩算法。     

SPIHT嵌入式编码算法在图像压缩中的应用

介绍了小波提升的基本原理及其实现流程,分析和比较了小波提升和Mallat算法在图像压缩中的应用,并将9/7提升小波应用在SPIHT嵌入式图像编码中。实验结果表明,该算法具有低复杂、高保真的特点。     

一种新颖的中子辐照图像压缩算法

分析了中子辐照图像的持点,提出一种适合于数字中子照相系统的图像压缩算法。将小波和方向滤波器组（DFB）结合,得到非冗余的图像变换WDFB;通过WDFB系数的重新组合,提出一种新颖的图像压缩方案,该方案类似于无表零树编码（LZC）算法;为了充分利用WDFB变换对二维分段光滑函数的良好表达能力,该算法引入线性索引技术并采用不同于LZC算法的零树表达策略。实验结果表明,该算法用于中子辐照图像压缩是有效的,在相同压缩比下,其主观视觉质量和峰值信噪比（PSNR）都明显优于LZC算法,和多级树集合分裂编码（SPIHT）算法相比,具有相当的压缩性能但更易于硬件实现。     

图像编码中的快速矢量变换算法

对图像采样数据进行矢量变换是实现图像矢量编码的重要组成部分.本文针对矢量变换算法中存在着由矢量构成矩阵的维数和变换核矩阵维数不相等的问题,提出了一种新的矢量变换算法并对该算法的解相关能力和能量压缩能力进行了统计分析.仿真实验结果表明该算法能较大地提高计算效率.     

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.     

一维DCT变换编码的有限字长效应分析

本文对一维DCT变换压缩编码中变换域系数二次量化引起的图象损伤进行了频域和空间域分析，得出了一些有用的结论．通过对变换矩阵系数字长的分析和计算机模拟，为一维DCT变换压缩编码硬件实现时字长的确定提供了依据．硬件实验结果印证了上述分析的结论．     

基于能量树编码的小波图像压缩算法

针对小波变换的空间能量聚集特性,提出了一种基于能量树编码的小波图像压缩算法。该算法在离散小波变换的基础上,分别对图像的各高频子带按其局部能量构建分层能量树,利用总能量和各层的能量角等效表示子带的小波系数;根据给定的压缩比,选择合适的代价函数构建最佳能量树,然后对其进行量化和编码,通过自适应的比特率分配实现小波图像压缩。实验结果表明,该算法实现简单,重构图像质量好,与当前多种主流的小波图像压缩算法相比,压缩性能有了明显提高。     

Vector Lifting Schemes for Stereo Image Coding

Many research efforts have been devoted to the improvement of stereo image coding techniques for storage or transmission. In this paper, we are mainly interested in lossy-to-lossless coding schemes for stereo images allowing progressive reconstruction. The most commonly used approaches for stereo compression are based on disparity compensation techniques. The basic principle involved in this technique first consists of estimating the disparity map. Then, one image is considered as a reference and the other is predicted in order to generate a residual image. In this paper, we propose a novel approach, based on vector lifting schemes (VLS), which offers the advantage of generating two compact multiresolution representations of the left and the right views. We present two versions of this new scheme. A theoretical analysis of the performance of the considered VLS is also conducted. Experimental results indicate a significant improvement using the proposed structures compared with conventional methods.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

基于脊波变换的图像压缩算法

自然图像包括大量的具有明显“直线边缘”的图像,而且边缘表示了图像的主要信息。利用脊波对“直线奇异”的良好刻画,针对具有直线特征的图像,设计基于脊波变换的有损压缩算法。首先对图像进行脊波变换,然后对变换系数进行标量量化、扫描、游程编码和熵编码。仿真实验表明,与基于小波变换的JPEG 2000压缩算法相比,该算法能获得更高的压缩率,同时保持较高的信噪比。     

基于Karhunen-Loeve和小波变换的多光谱图像三维集合嵌入块编码压缩算法

目前多光谱成像已被广泛应用在遥感领域中,但巨量的多光谱图像数据导致存储和传输的困难,这就需要对多光谱图像数据进行高效的压缩.该文提出了一种新的多光谱图像压缩算法(KLT/WT-3DSPECK),首先利用二维小波变换(2DWT)去除多光谱图像的空间相关冗余,然后利用Karhunen-Loeve变换(KLT)减少频域的相关性,最后对变换后的系数利用改进的三维集合块分裂算法(3DSPECK)进行编码.根据变换后子带能量分布的特点,提出了新的3D八度音符分裂方法(octave partitioning)和改进的集合零块分裂算法.为了加速本文算法和优化嵌入码流的率失真性能,还给出了一种零块优化排序的快速算法.对两组多光谱和超光谱图像的测试表明,该算法不仅明显优于KLT/WT-3DSPIHT算法,也远优于基于3D小波变换的AT-3DSPIHT和3DSPECK等算法,同时该算法还支持图像的渐进传输.     

曲波变换和最小二乘支持向量机的图像压缩算法

为了提高图像压缩质量,针对传统压缩算法的不足,提出一种曲波变换和最小二乘支持向量机相融合的图像压缩算法。首先采用曲波变换把图像分解为不同尺度和不同方向的曲波系数,并采用熵编码对粗尺度层曲波系数进行压缩,然后利用最小二乘支持向量机对细尺度层中不同方向的曲波系数进行学习,并通过和声搜索算法优化最小二乘支持向量机,实现细尺度层曲波数的压缩,最后采用图像压缩仿真实验测试其性能。结果表明,曲波变换和最小二乘支持向量机相融合的图像压缩算法提高了图像压缩的峰值信噪比,加快了图像压缩的速度,获得了更好的图像压缩效果。     

Embedded image compression based on wavelet pixel classification and sorting

The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.     

基于Contourlet变换的自适应门限分形零树混合图像编码

Contourlet变换的基函数分布于多尺度、多方向上,弥补了小波基函数方向性不足的缺陷.本文考虑了分形编码过程中子树与父树匹配误差门限对编码的影响,提出了一种基于Contourlet变换的自适应门限分形零树混合图像编码方法,给出了所提出方法的实验结果,并与基于小波变换的分形零树混合图像编码方法进行了比较.实验结果表明,在压缩比相同的情况下,本文方法的PSNR性能优于基于小波变换的分形零树混合图像编码方法,能很好地保留图像的纹理和轮廓.     

Image compression based on energy clustering and zero-quadtreerepresentation

An efficient image compression algorithm based on energy clustering and zero-quadtree representation (ECZQR) in the wavelet transform domain is proposed. In embedded coding, zeros within each subband are encoded in the framework of quadtree representation instead of zerotree representation. To use large rectangular blocks to represent zeros, it first uses morphological dilation to extract the arbitrarily shaped clusters of significant coefficients within each subband. The proposed encoding method results in less distortion in the decoded image than the line-by-line encoding method. Experimental results show that the algorithm is among the most efficient wavelet image compression algorithms