Video compression using spatiotemporal regularity flow.

We propose a new framework in wavelet video coding to improve the compression rate by exploiting the spatiotemporal regularity of the data. A sequence of images creates a spatiotemporal volume. This volume is said to be regular along the directions in which the pixels vary the least, hence the entropy is the lowest. The wavelet decomposition of regularized data results in a fewer number of significant coefficients, thus yielding a higher compression rate. The directions of regularity of an image sequence depend on both its motion content and spatial structure. We propose the representation of these directions by a 3-D vector field, which we refer to as the spatiotemporal regularity flow (SPREF). SPREF uses splines to approximate the directions of regularity. The compactness of the spline representation results in a low storage overhead for SPREF, which is a desired property in compression applications. Once SPREF directions are known, they can be converted into actual paths along which the data is regular. Directional decomposition of the data along these paths can be further improved by using a special class of wavelet basis called the 3-D orthonormal bandelet basis. SPREF -based video compression not only removes the temporal redundancy, but it also compensates for the spatial redundancy. Our experiments on several standard video sequences demonstrate that the proposed method results in higher compression rates as compared to the standard wavelet based compression.     

Synthetic aperture radar image compression using tree-structured edge-directed orthogonal wavelet packet transform

Currently, wavelet-based coding algorithms are popular for synthetic aperture radar (SAR) image compression, which is very important for reducing the cost of data storage and transmission in relatively slow channels. However, standard wavelet transform is limited by spatial isotropy of its basis functions that is not completely adapted to represent image entities like edges or textures, which means wavelet-based coding algorithms are suboptimal to image compression. In this paper, a novel tree-structured edge-directed orthogonal wavelet packet transform is proposed for SAR image compression. Inspired by the intrinsic geometric structure of images, the new transform improves the performance of standard wavelet by filtering along the regular direction first and then along the orthogonal direction with directional lifting structure. The cost function of best basis selection is designed by textural and directional information for tree-structured edge-directed orthogonal wavelet packet transform. The new transform including speckle reduction can be used to construct SAR image coder with the embedded block coding with optimal truncation for transform coefficients, and arithmetic coding for additional information. The experimental results show that the proposed approach outperforms JPEG2000 and Fast wavelet packet (FWP), both visually and item of PSNR values.     

基于第二代Bandelet变换的图像编码

Bandelet基函数可以实现几何正则图像的最佳稀疏表示,在图像压缩方面有很大潜力。为了得到更高的压缩比,在第二代Bandelet变换的研究基础上,提出了一种与Bandelet变换相适应的图像编码方案。首先对Bandelet系数采用一致量化,重新组织系数的排布方式,然后设计了一种针对Bandelet系数的上下文模型,进行基于上下文的编码。对低频子带采用CALIC模型进行编码。实验表明,该编码方法对自然图像压缩是有效的,特别是对几何流特征比较明显的图像取得了优于JPEG 2000的结果。     

一种改进的嵌入式图像EZW编码算法

介绍了在利用小波变换进行图像压缩时,小波基选择一般需要考虑的几个方面.并通过实验数据说明了不同的小波基对BMP类图像压缩产生的影响.然后对基于小波变换的嵌入式图像编码方法中最具代表性的EZW编码算法的原理进行了分析,并通过实验对EZW算法进行了性能分析及其改进,给出了实验结果.     

采用格形算法对SAR图像进行压缩处理

文章提出了一种新的可用于合成孔径雷达的图像数据压缩技术。该技术基于格形编码的方案，人有比以往的压缩算法更为出色的特性，在星载和机载合成孔径雷达图像和原始数据的压缩上有着广泛的应用前景。文中给出了对机载雷达图像压缩后的实验结果和分析数据。     

Fast full search equivalent encoding algorithms for imagecompression using vector quantization

Three fast search routines to be used in the encoding phase of vector quantization (VQ) image compression systems are presented. These routines, which are based on geometric considerations, provide the same results as an exhaustive (or full) search. Examples show that the proposed algorithms need only 3-20% of the number of mathematical operations required by a full search and fewer than 50% of the operations required by recently proposed alternatives.     

Joint space-frequency segmentation using balanced wavelet packettrees for least-cost image representation

We examine the question of how to choose a space varying filterbank tree representation that minimizes some additive cost function for an image. The idea is that for a particular cost function, e.g., energy compaction or quantization distortion, some tree structures perform better than others. While the wavelet tree represents a good choice for many signals, it is generally outperformed by the best tree from the library of wavelet packet frequency-selective trees. The double-tree library of bases performs better still, by allowing different wavelet packet trees over all binary spatial segments of the image. We build on this foundation and present efficient new pruning algorithms for both one- and two-dimensional (1-D and 2-D) trees that will find the best basis from a library that is many times larger than the library of the single-tree or double-tree algorithms. The augmentation of the library of bases overcomes the constrained nature of the spatial variation in the double-tree bases, and is a significant enhancement in practice. Use of these algorithms to select the least-cost expansion for images with a rate-distortion cost function gives a very effective signal adaptive compression scheme. This scheme is universal in the sense that, without assuming a model for the signal or making use of training data, it performs very well over a large class of signal types. In experiments it achieves compression rates that are competitive with the best training-based schemes.     

Complexity-constrained best-basis wavelet packet algorithm forimage compression

The concept of adapted waveform analysis using a best-basis selection out of a predefined library of wavelet packet (WP) bases allows an efficient image representation for the purpose of compression. Image coding methods based on the best-basis WP representation have shown significant coding gains for some image classes compared with methods using a fixed dyadic structured wavelet basis, at the expense however, of considerably higher computational complexity. A modification of the best-basis method, the so-called complexity constrained best-basis algorithm (CCBB), is proposed which parameterises the complexity gap between the fast (standard) wavelet transform and the best wavelet packet basis of a maximal WP library. This new approach allows a `suboptimal' best basis to be found with respect to a given budget of computational complexity or, in other words, it offers an instrument to control the trade-off between compression speed and, coding efficiency. Experimental results are presented for image coding applications showing a highly nonlinear relationship between the rate-distortion performance and the computational complexity in such a way that a relatively small increase in complexity with respect to the standard wavelet basis results in a relatively high rate distortion gain     

Lapped multiple bases algorithms for still image compressionwithout blocking effect

We describe a system for still image compression that uses several transform sets in a multiple bases realization algorithm. Our algorithms reduce the number of encoded transform coefficients 20% beyond DCT-only compression. We extend these algorithms to use several newly developed lapped orthogonal transform (LOT) bases, resulting in useful algorithms for low bit rate (high compression) operation without blocking effect.     

On the use of independent component analysis for image compression

This paper addresses the use of independent component analysis (ICA) for image compression. Our goal is to study the adequacy (for lossy transform compression) of bases learned from data using ICA. Since these bases are, in general, non-orthogonal, two methods are considered to obtain image representations: matching pursuit type algorithms and orthogonalization of the ICA bases followed by standard orthogonal projection.Several coder architectures are evaluated and compared, using both the usual SNR and a perceptual quality measure called picture quality scale. We consider four classes of images (natural, faces, fingerprints, and synthetic) to study the generalization and adaptation abilities of the data-dependent ICA bases. In this study, we have observed that: bases learned from natural images generalize well to other classes of images; bases learned from the other specific classes show good specialization. For example, for fingerprint images, our coders perform close to the special-purpose WSQ coder developed by the FBI. For some classes, the visual quality of the images obtained with our coders is similar to that obtained with JPEG2000, which is currently the state-of-the-art coder and much more sophisticated than a simple transform coder.We conclude that ICA provides a excellent tool for learning a coder for a specific image class, which can even be done using a single image from that class. This is an alternative to hand tailoring a coder for a given class (as was done, for example, in the WSQ for fingerprint images). Another conclusion is that a coder learned from natural images acts like an universal coder, that is, generalizes very well for a wide range of image classes.     

A chip set for lossless image compression

The authors describe two chips which form the basis of a high-speed lossless image compression/decompression system. They present the transform and coding algorithms and the main architectural features of the chips and outline some performance specifications. Lossless compression can be achieved by a transformation process followed by entropy coding. The two application-specific integrated circuits (ASICs) perform S-transform image decomposition and the Lempel-Ziv (L-Z) type of entropy coding. The S-transform, besides decorrelating the image, provides a convenient method of hierarchical image decomposition. The data compressor/decompressor IC is a fast and efficient implementation of the L-Z algorithm. The chips can be used independently or together for image compression     

Satellite image compression by post-transforms in the wavelet domain

This paper proposes a novel compression scheme with a tunable complexity-rate-distortion trade-off. As images increase in size and resolution, more efficient compression schemes with low complexity are required on-board Earth observation satellites. The standard of the Consultative Committee for Space Data Systems (CCSDS) defines a strip-based compression scheme with the advantages of a low complexity and an easy rate control [CCSDS, Image Data Compression Recommended Standard CCSDS 122.0-B-1 Blue Book, November 2005]. However, future mission specifications expect higher performance in terms of rate-distortion. The scheme proposed in this paper intends to perform better than the CCSDS standard while preserving low complexity and easy rate control. Moreover, to comply with existing on-board devices, the proposed core compression engine still uses the wavelet transform but in association with a linear post-processing inspired from the bandelet transform. The post-transform decomposes a small block of wavelet coefficients on a particular basis. This basis is adaptively selected within a predefined dictionary by rate-distortion optimization. The computational complexity depends upon the dictionary size and of the basis structure. An extremely simple dictionary, reduced to the Hadamard basis, is proposed. The post-transform efficiency is illustrated by experiments on various Earth observation images provided by the French Space Agency (CNES).     

Fast algorithm for the 3-D DCT-II

Recently, many applications for three-dimensional (3-D) image and video compression have been proposed using 3-D discrete cosine transforms (3-D DCTs). Among different types of DCTs, the type-II DCT (DCT-II) is the most used. In order to use the 3-D DCTs in practical applications, fast 3-D algorithms are essential. Therefore, in this paper, the 3-D vector-radix decimation-in-frequency (3-D VR DIF) algorithm that calculates the 3-D DCT-II directly is introduced. The mathematical analysis and the implementation of the developed algorithm are presented, showing that this algorithm possesses a regular structure, can be implemented in-place for efficient use of memory, and is faster than the conventional row-column-frame (RCF) approach. Furthermore, an application of 3-D video compression-based 3-D DCT-II is implemented using the 3-D new algorithm. This has led to a substantial speed improvement for 3-D DCT-II-based compression systems and proved the validity of the developed algorithm.     

Coding of arbitrarily shaped image segments based on a generalized orthogonal transform

Region oriented image representation offers several advantages over block-oriented schemes, e.g. adaptation to the local image characteristics, or object motion compensation as opposed to block-wise motion compensation. For the task of image data compression, i.e. image coding, new algorithms are needed which work on arbitrarily shaped image regions, called segments, instead of rectangular image blocks. Based on a generalized moment approach, the luminance function inside the segment is approximated by a weighted sum of basis functions, for example polynomials. A set of basis functions which is orthogonal with respect to the shape of the segment to be coded can be obtained using orthogonalization schemes. This results in the derivation of a generalized shape-adapted transform coder. Suitable coder and decoder structures are introduced which do not necessitate the transmission of the basis functions for each segment. Finally an application of the derived algorithms to image sequence coding at low data rates is shown, which is based on a segmentation of the motion compensated prediction error image.     

基于游程编码的分块交叉数字图像压缩算法

本文在软件工程设计中发现了一种数字图像压缩算法，它是在游程编码基础上的改进和创新，具有许多明显的优点，如无损压缩、高效、快速等。实际应用表明，该算法具有很好的适用性。     

Best basis search in lapped dictionaries

This paper proposes, analyzes, and illustrates several best basis search algorithms for dictionaries consisting of lapped orthogonal bases. It improves upon the best local cosine basis selection based on a dyadic tree , by considering larger dictionaries of bases. It is shown that this can result in sparser representations and approximate shift invariance. An algorithm that is strictly shift invariant is also provided. The experiments in this paper suggest that the new dictionaries can be advantageous for time-frequency analysis, compression, and noise removal. Accelerated versions of the basic algorithm are provided that explore various tradeoffs between computational efficiency and adaptability. It is shown that the proposed algorithms are in fact applicable to any finite dictionary comprised of lapped orthogonal bases. One such novel dictionary is proposed that constructs the best local cosine representation in the frequency domain, and it is shown that the new dictionary is better suited for representing certain types of signals.     

基于DSP的自动调焦系统

由于数字图像处理理论的逐渐成熟和完善,现今的自动调焦系统已由原基于光学系统的自动调焦转换为基于各种数字图像处理方法的自动调焦.由此介绍一种基于DSP芯片TMS320C6416为核心处理器进行图像采集、预处理和实施控制的自动调焦系统.TMS320C6416芯片的总体性能可比C62xx提高一个数量级.重点说明了该实时数字图像处理系统的硬件组成、工作原理和软件算法.结果表明该系统具有高度的实时性和稳定性,能够快速准确的完成自动调焦.     

基于Multibandelets的自适应图像压缩

为了在图像压缩时更好地保护具有方向性的几何结构信息,该文构建了一种新的基函数,称为Multibandelets,并结合Shannon编码用于自然图像的压缩。实验结果表明：与多小波、具有同样消失矩的小波和Bandelets相比较,基于Multibandelets的图像压缩在视觉效果和客观衡量指标两方面都有改善,尤其对具有方向性的细节和纹理信息具有更好的表示。     

Geometric Invariance in image watermarking

Surviving geometric attacks in image watermarking is considered to be of great importance. In this paper, the watermark is used in an authentication context. Two solutions are being proposed for such a problem. Both geometric and invariant moments are used in the proposed techniques. An invariant watermark is designed and tested against attacks performed by StirMark using the invariant moments. On the other hand, an image normalization technique is also proposed which creates a normalized environment for watermark embedding and detection. The proposed algorithms have the advantage of being robust, computationally efficient, and no overhead needs to be transmitted to the decoder side. The proposed techniques have proven to be highly robust to all geometric manipulations, filtering, compression and slight cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt & pepper.