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。     

Context-based entropy coding of block transform coefficients for image compression

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. Wavelet-based JPEG2000 is emerging as the new high-performance international standard for still image compression. An often asked question is: how much of the coding improvement is due to the transform and how much is due to the encoding strategy? Current block transform coders such as JPEG suffer from poor context modeling and fail to take full advantage of correlation in both space and frequency sense. This paper presents a simple, fast, and efficient adaptive block transform image coding algorithm based on a combination of prefiltering, postfiltering, and high-order space-frequency context modeling of block transform coefficients. Despite the simplicity constraints, coding results show that the proposed coder achieves competitive R-D performance compared to the best wavelet coders in the literature.     

Context conditioning and run-length coding for hybrid, embeddedprogressive image coding

An analysis of spatial context conditioning and run-length coding in embedded progressive, wavelet-based image coding is presented. The analysis shows that run-length coding of certain context subsequences is superior to pure entropy coding, both in terms of coding performance and of execution speed. Based on these considerations, a novel, intuitive context conditioning scheme using a spatial distance model to describe the statistics of significant coefficients is proposed. The results for the proposed coding scheme are competitive to the best coding schemes found in the literature.     

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     

Oriented wavelet transform for image compression and denoising.

In this paper, we introduce a new transform for image processing, based on wavelets and the lifting paradigm. The lifting steps of a unidimensional wavelet are applied along a local orientation defined on a quincunx sampling grid. To maximize energy compaction, the orientation minimizing the prediction error is chosen adaptively. A fine-grained multiscale analysis is provided by iterating the decomposition on the low-frequency band. In the context of image compression, the multiresolution orientation map is coded using a quad tree. The rate allocation between the orientation map and wavelet coefficients is jointly optimized in a rate-distortion sense. For image denoising, a Markov model is used to extract the orientations from the noisy image. As long as the map is sufficiently homogeneous, interesting properties of the original wavelet are preserved such as regularity and orthogonality. Perfect reconstruction is ensured by the reversibility of the lifting scheme. The mutual information between the wavelet coefficients is studied and compared to the one observed with a separable wavelet transform. The rate-distortion performance of this new transform is evaluated for image coding using state-of-the-art subband coders. Its performance in a denoising application is also assessed against the performance obtained with other transforms or denoising methods.     

几种小波图像编码方法的比较

小波变换具有良好的时频或空频局部特性,特别适合于按照人眼视觉系统特性设计的图像/视频编码方案.随着MPEG-4和JPEG-2000等基于小波变换的图像/视频编码标准的制定和推广,小波编码技术将被广泛采用.目前小波图像编码的方法有很多,其中嵌入式编码方法得到了人们的广泛关注.本文首先介绍EZW、SPIHT、SPECK等七种小波编码器的原理和方法,然后对这几种小波编码器的性能进行了比较.     

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。     

Computation of the coding gain for subband coders

A procedure to evaluate the coding gain for 2-D subband systems is explicitly presented. The technique operates in the signal domain and requires the knowledge of the input process auto-correlation function. Both the case of uniform subband and pyramid decomposition are considered. In the case of a separable input process spectrum, the evaluation can be performed by considering appropriately defined 1-D systems, thus, making the procedure very convenient in terms of computational complexity. Using a model that has been recently derived for difference images in motion-compensated image sequence coders, we compare the performance of several filter banks and transform coders in terms of coding gain and asymptotic rate-distortion figures. The results for intraframe and interframe coding show that uniform subband coders can have a performance superior to that of transform coders. Pyramidal schemes appear to have a slightly worse performance     

Joint thresholding and quantizer selection for transform imagecoding: entropy-constrained analysis and applications to baseline JPEG

Striving to maximize baseline (Joint Photographers Expert Group-JPEG) image quality without compromising compatibility of current JPEG decoders, we develop an image-adaptive JPEG encoding algorithm that jointly optimizes quantizer selection, coefficient "thresholding", and Huffman coding within a rate-distortion (R-D) framework. Practically speaking, our algorithm unifies two previous approaches to image-adaptive JPEG encoding: R-D optimized quantizer selection and R-D optimal thresholding. Conceptually speaking, our algorithm is a logical consequence of entropy-constrained vector quantization (ECVQ) design principles in the severely constrained instance of JPEG-compatible encoding. We explore both viewpoints: the practical, to concretely derive our algorithm, and the conceptual, to justify the claim that our algorithm approaches the best performance that a JPEG encoder can achieve. This performance includes significant objective peak signal-to-noise ratio (PSNR) improvement over previous work and at high rates gives results comparable to state-of-the-art image coders. For example, coding the Lena image at 1.0 b/pixel, our JPEG encoder achieves a PSNR performance of 39.6 dB that slightly exceeds the quoted PSNR results of Shapiro's wavelet-based zero-tree coder. Using a visually based distortion metric, we can achieve noticeable subjective improvement as well. Furthermore, our algorithm may be applied to other systems that use run-length encoding, including intraframe MPEG and subband or wavelet coding.     

Robust rate-control for wavelet-based image coding via conditional probability models.

Real-time rate-control for wavelet image coding requires characterization of the rate required to code quantized wavelet data. An ideal robust solution can be used with any wavelet coder and any quantization scheme. A large number of wavelet quantization schemes (perceptual and otherwise) are based on scalar dead-zone quantization of wavelet coefficients. A key to performing rate-control is, thus, fast, accurate characterization of the relationship between rate and quantization step size, the R-Q curve. A solution is presented using two invocations of the coder that estimates the slope of each R-Q curve via probability modeling. The method is robust to choices of probability models, quantization schemes and wavelet coders. Because of extreme robustness to probability modeling, a fast approximation to spatially adaptive probability modeling can be used in the solution, as well. With respect to achieving a target rate, the proposed approach and associated fast approximation yield average percentage errors around 0.5% and 1.0% on images in the test set. By comparison, 2-coding-pass rho-domain modeling yields errors around 2.0%, and post-compression rate-distortion optimization yields average errors of around 1.0% at rates below 0.5 bits-per-pixel (bpp) that decrease down to about 0.5% at 1.0 bpp; both methods exhibit more competitive performance on the larger images. The proposed method and fast approximation approach are also similar in speed to the other state-of-the-art methods. In addition to possessing speed and accuracy, the proposed method does not require any training and can maintain precise control over wavelet step sizes, which adds flexibility to a wavelet-based image-coding system.     

The coding gain of multiplexed wavelet transforms

Evangelista (1994, 1993) introduced the multiplexed wavelet transform (MWT) and pointed out its potential applications to the analysis, synthesis, processing and coding of pseudo-periodic signals such as voiced speech and music. Coders based on the MWT have been shown to outperform the conventional subband coders when a reliable pitch parameter can be extracted from data. In this paper, we investigate the effects of uniform quantization of the MWT coefficients. We compare the performance of the new coders with that of WT, block-DCT, and KLT coders in terms of the coding gain achieved when optimal bit allocation schemes are adopted     

On exploiting geometric constraint of image wavelet coefficients

We investigate the problem of how to exploit the geometric constraints of edges in wavelet-based image coding. The value of studying this problem is the potential coding gain brought by improved probabilistic models of wavelet high-band coefficients. Novel phase shifting and prediction algorithms are derived in the wavelet space. It is demonstrated that after resolving the phase uncertainty, high-band wavelet coefficients can be better modeled by biased-mean probability models rather than the existing zero-mean ones. In lossy coding, the coding gain brought by the biased-mean model is quantitatively analyzed within the conventional DPCM coding framework. Experimental results show that the proposed phase shifting and prediction scheme improves both the subjective and objective performance of wavelet-based image coders.     

Best wavelet packet bases in a rate-distortion sense

A fast rate-distortion (R-D) optimal scheme for coding adaptive trees whose individual nodes spawn descendents forming a disjoint and complete basis cover for the space spanned by their parent nodes is presented. The scheme guarantees operation on the convex hull of the operational R-D curve and uses a fast dynamic programing pruning algorithm to markedly reduce computational complexity. Applications for this coding technique include R. Coefman et al.'s (Yale Univ., 1990) generalized multiresolution wavelet packet decomposition, iterative subband coders, and quadtree structures. Applications to image processing involving wavelet packets as well as discrete cosine transform (DCT) quadtrees are presented.     

基于子带金字塔结构的彩色图像小波编码

本文改进了分层树集分割算法(SPIHT),重新设计了一种子带金字塔的数据结构来组织图像的二维小波变换系数,既减少了存储空间又加快了编码速度.在彩色图像的YUV空间按位平面的顺序依次对YUV三个分量编码,输出颜色分量完全嵌入式的码流.实验结果表明改进后算法对彩色图像的编码能获得很好的率失真性能,而且适用于高分辨率、不规则图像的编解码.     

Low Bit-Rate Design Considerations for Wavelet-Based Image Coding

Biorthogonal and orthogonal filter pairs derived from the family of binomial product filters are considered for wavelet transform implementation with the goal of high performance lossy image compression. Using experimental rate-distortion performance as the final measure of comparison, a number of new and existing filters are presented with excellent image coding capabilities. In addition, numerous filter attributes such as orthonormality, transition band sharpness, coding gain, low-band reconstruction error, regularity, and vanishing moments are assessed to determine their importance with regards to the fidelity of the decoded images. While image data compression is specifically addressed, many of the proposed techniques are applicable to other coding applications.     

Wavelet image coding based on significance extraction usingmorphological operations

An algorithm is proposed for improving Servetto et al.'s (see Proceedings of the International Conference on Image Processing, Washington, DC, p.530-3, 1995) method of morphological representation of wavelet data (MRWD), which is among the most efficient wavelet-based image compression algorithms. In MRWD, morphological dilation is used to capture and encode the arbitrarily shaped clusters of significant coefficients within each subband and high compression is achieved. But there are still several deficiencies for rectification in MRWD. An efficient image compression algorithm is proposed, in which, for each subband, morphological dilation is first used to extract and encode the clustered significant coefficients, and the remaining space is encoded in an efficient way. Instead of encoding the large number of zeros one by one, only the small number of remaining significant coefficients and their positional information are encoded. Experimental results show that this improvement is very effective, especially for images with large and relatively smooth regions     

Wavelet image coding using variable blocksize vector quantization with optimal quadtree segmentation

In this paper, we propose an image coding scheme by using the variable blocksize vector quantization (VBVQ) to compress wavelet coefficients of an image. The scheme is capable of finding an optimal quadtree segmentation of wavelet coefficients of an image for VBVQ subject to a given bit budget, such that the total distortion of quantized wavelet coefficients is minimal. From our simulation results, we can see that our proposed coding scheme has higher performance in PSNR than other wavelet/VQ or subband/VQ coding schemes.     

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.     

Skeleton-based morphological coding of binary images

This paper presents new properties of the discrete morphological skeleton representation of binary images, along with a novel coding scheme for lossless binary image compression that is based on these properties. Following a short review of the theoretical background, two sets of new properties of the discrete morphological skeleton representation of binary images are proved. The first one leads to the conclusion that only the radii of skeleton points belonging to a subset of the ultimate erosions are needed for perfect reconstruction. This corresponds to a lossless sampling of the quench function. The second set of new properties is related to deterministic prediction of skeletonal information in a progressive transmission scheme. Based on the new properties, a novel coding scheme for binary images is presented. The proposed scheme is suitable for progressive transmission and fast implementation. Computer simulations, also presented, show that the proposed coding scheme substantially improves the results obtained by previous skeleton-based coders, and performs better than classical coders, including run-length/Huffman, quadtree, and chain coders. For facsimile images, its performance can be placed between the modified read (MR) method (K=4) and modified modified read (MMR) method.