Showing papers on "Data compression published in 1996"

A new, fast, and efficient image codec based on set partitioning in hierarchical trees

Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.

Introduction to data compression

Abstract: Preface 1 Introduction 1.1 Compression Techniques 1.1.1 Lossless Compression 1.1.2 Lossy Compression 1.1.3 Measures of Performance 1.2 Modeling and Coding 1.3 Organization of This Book 1.4 Summary 1.5 Projects and Problems 2 Mathematical Preliminaries 2.1 Overview 2.2 A Brief Introduction to Information Theory 2.3 Models 2.3.1 Physical Models 2.3.2 Probability Models 2.3.3. Markov Models 2.3.4 Summary 2.5 Projects and Problems 3 Huffman Coding 3.1 Overview 3.2 "Good" Codes 3.3. The Huffman Coding Algorithm 3.3.1 Minimum Variance Huffman Codes 3.3.2 Length of Huffman Codes 3.3.3 Extended Huffman Codes 3.4 Nonbinary Huffman Codes 3.5 Adaptive Huffman Coding 3.5.1 Update Procedure 3.5.2 Encoding Procedure 3.5.3 Decoding Procedure 3.6 Applications of Huffman Coding 3.6.1 Lossless Image Compression 3.6.2 Text Compression 3.6.3 Audio Compression 3.7 Summary 3.8 Projects and Problems 4 Arithmetic Coding 4.1 Overview 4.2 Introduction 4.3 Coding a Sequence 4.3.1 Generating a Tag 4.3.2 Deciphering the Tag 4.4 Generating a Binary Code 4.4.1 Uniqueness and Efficiency of the Arithmetic Code 4.4.2 Algorithm Implementation 4.4.3 Integer Implementation 4.5 Comparison of Huffman and Arithmetic Coding 4.6 Applications 4.6.1 Bi-Level Image Compression-The JBIG Standard 4.6.2 Image Compression 4.7 Summary 4.8 Projects and Problems 5 Dictionary Techniques 5.1 Overview 5.2 Introduction 5.3 Static Dictionary 5.3.1 Diagram Coding 5.4 Adaptive Dictionary 5.4.1 The LZ77 Approach 5.4.2 The LZ78 Approach 5.5 Applications 5.5.1 File Compression-UNIX COMPRESS 5.5.2 Image Compression-the Graphics Interchange Format (GIF) 5.5.3 Compression over Modems-V.42 bis 5.6 Summary 5.7 Projects and Problems 6 Lossless Image Compression 6.1 Overview 6.2 Introduction 6.3 Facsimile Encoding 6.3.1 Run-Length Coding 6.3.2 CCITT Group 3 and 4-Recommendations T.4 and T.6 6.3.3 Comparison of MH, MR, MMR, and JBIG 6.4 Progressive Image Transmission 6.5 Other Image Compression Approaches 6.5.1 Linear Prediction Models 6.5.2 Context Models 6.5.3 Multiresolution Models 6.5.4 Modeling Prediction Errors 6.6 Summary 6.7 Projects and Problems 7 Mathematical Preliminaries 7.1 Overview 7.2 Introduction 7.3 Distortion Criteria 7.3.1 The Human Visual System 7.3.2 Auditory Perception 7.4 Information Theory Revisted 7.4.1 Conditional Entropy 7.4.2 Average Mutual Information 7.4.3 Differential Entropy 7.5 Rate Distortion Theory 7.6 Models 7.6.1 Probability Models 7.6.2 Linear System Models 7.6.3 Physical Models 7.7 Summary 7.8 Projects and Problems 8 Scalar Quantization 8.1 Overview 8.2 Introduction 8.3 The Quantization Problem 8.4 Uniform Quantizer 8.5 Adaptive Quantization 8.5.1 Forward Adaptive Quantization 8.5.2 Backward Adaptive Quantization 8.6 Nonuniform Quantization 8.6.1 pdf-Optimized Quantization 8.6.2 Companded Quantization 8.7 Entropy-Coded Quantization 8.7.1 Entropy Coding of Lloyd-Max Quantizer Outputs 8.7.2 Entropy-Constrained Quantization 8.7.3 High-Rate Optimum Quantization 8.8 Summary 8.9 Projects and Problems 9 Vector Quantization 9.1 Overview 9.2 Introduction 9.3 Advantages of Vector Quantization over Scalar Quantization 9.4 The Linde-Buzo-Gray Algorithm 9.4.1 Initializing the LBG Algorithm 9.4.2 The Empty Cell Problem 9.4.3 Use of LBG for Image Compression 9.5 Tree-Structured Vector Quantizers 9.5.1 Design of Tree-Structured Vector Quantizers 9.6 Structured Vector Quantizers 9.6.1 Pyramid Vector Quantization 9.6.2 Polar and Spherical Vector Quantizers 9.6.3 Lattice Vector Quantizers 9.7 Variations on the Theme 9.7.1 Gain-Shape Vector Quantization 9.7.2 Mean-Removed Vector Quantization 9.7.3 Classified Vector Quantization 9.7.4 Multistage Vector Quantization 9.7.5 Adaptive Vector Quantization 9.8 Summary 9.9 Projects and Problems 10 Differential Encoding 10.1 Overview 10.2 Introduction 10.3 The Basic Algorithm 10.4 Prediction in DPCM 10.5 Adaptive DPCM (ADPCM) 10.5.1 Adaptive Quantization in DPCM 10.5.2 Adaptive Prediction in DPCM 10.6 Delta Modulation 10.6.1 Constant Factor Adaptive Delta Modulation (CFDM) 10.6.2 Continuously Variable Slope Delta Modulation 10.7 Speech Coding 10.7.1 G.726 10.8 Summary 10.9 Projects and Problems 11 Subband Coding 11.1 Overview 11.2 Introduction 11.3 The Frequency Domain and Filtering 11.3.1 Filters 11.4 The Basic Subband Coding Algorithm 11.4.1 Bit Allocation 11.5 Application to Speech Coding-G.722 11.6 Application to Audio Coding-MPEG Audio 11.7 Application to Image Compression 11.7.1 Decomposing an Image 11.7.2 Coding the Subbands 11.8 Wavelets 11.8.1 Families of Wavelets 11.8.2 Wavelets and Image Compression 11.9 Summary 11.10 Projects and Problems 12 Transform Coding 12.1 Overview 12.2 Introduction 12.3 The Transform 12.4 Transforms of Interest 12.4.1 Karhunen-Loeve Transform 12.4.2 Discrete Cosine Transform 12.4.3 Discrete Sine Transform 12.4.4 Discrete Walsh-Hadamard Transform 12.5 Quantization and Coding of Transform Coefficients 12.6 Application to Image Compression-JPEG 12.6.1 The Transform 12.6.2 Quantization 12.6.3 Coding 12.7 Application to Audio Compression 12.8 Summary 12.9 Projects and Problems 13 Analysis/Synthesis Schemes 13.1 Overview 13.2 Introduction 13.3 Speech Compression 13.3.1 The Channel Vocoder 13.3.2 The Linear Predictive Coder (Gov.Std.LPC-10) 13.3.3 Code Excited Linear Prediction (CELP) 13.3.4 Sinusoidal Coders 13.4 Image Compression 13.4.1 Fractal Compression 13.5 Summary 13.6 Projects and Problems 14 Video Compression 14.1 Overview 14.2 Introduction 14.3 Motion Compensation 14.4 Video Signal Representation 14.5 Algorithms for Videoconferencing and Videophones 14.5.1 ITU_T Recommendation H.261 14.5.2 Model-Based Coding 14.6 Asymmetric Applications 14.6.1 The MPEG Video Standard 14.7 Packet Video 14.7.1 ATM Networks 14.7.2 Compression Issues in ATM Networks 14.7.3 Compression Algorithms for Packet Video 14.8 Summary 14.9 Projects and Problems A Probability and Random Processes A.1 Probability A.2 Random Variables A.3 Distribution Functions A.4 Expectation A.5 Types of Distribution A.6 Stochastic Process A.7 Projects and Problems B A Brief Review of Matrix Concepts B.1 A Matrix B.2 Matrix Operations C Codes for Facsimile Encoding D The Root Lattices Bibliography Index

Digital Video: An introduction to MPEG-2

Abstract: From the Publisher: This book offers comprehensive coverage of the MPEG-2 audio / visual digital compression standard. The treatment includes the specifics needed to implement an MPEG-2 Decoder, including the syntax and semantics of the coded bitstreams. Since the MPEG-2 Encoders are not specified by the standard, and are actually closely held secrets of many vendors, the book only outlines the fundamentals of encoder design and algorithm optimization.

An image multiresolution representation for lossless and lossy compression

Abstract: We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation, the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate versus distortion performance is comparable to those of the most efficient lossy compression methods.

Comparison of video shot boundary detection techniques

Abstract: Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper presents a comparison of several shot boundary detection and classification techniques and their variations including histograms, discrete cosine transform, motion vector, and block matching methods. The perfor- mance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with a good mix of transition types. Threshold selection requires a trade-off between recall and precision that must be guided by the target application. © 1996 SPIE and IS&T.

LOCO-I: a low complexity, context-based, lossless image compression algorithm

Abstract: LOCO-I (low complexity lossless compression for images) is a novel lossless compression algorithm for continuous-tone images which combines the simplicity of Huffman coding with the compression potential of context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with a collection of (context-conditioned) Huffman codes, which is realized with an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains, in one pass, and without recourse to the higher complexity arithmetic coders, compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. In fact, LOCO-I is being considered by the ISO committee as a replacement for the current lossless standard in low-complexity applications.

A robust content based digital signature for image authentication

Abstract: A methodology for designing content based digital signatures which can be used to authenticate images is presented. A continuous measure of authenticity is presented which forms the basis of this methodology. Using this methodology signature systems can be designed which allow certain types of image modification (e.g. lossy compression) but which prevent other types of manipulation. Some experience with content based signatures is also presented. The idea of signature based authentication is extended to video, and a system to generate signatures for video sequences is presented. This signature also allows smaller segments of the secured video to be verified as unmanipulated.

MPEG Video: Compression Standard

Abstract: From the Publisher: For all those interested in high definition television, multimedia, and image compression, this unique reference will be an essential tool It provides the first comprehensive introduction to this field, incorporating material ranging from basic concerns of newcomers to the field through sophisticated reviews of cutting edge technical issues Written by acknowledged experts in the field, MPEG Video Compression Standard offers important benefits to readers including detailed information on MPEG modes of operation, signaling conventions, and structure of MPEG compressed data Each section of the book is labeled by level of technical difficulty, allowing less technical readers to skip higher level sections and still gain a broad understanding of the subject while guiding advanced readers to the in-depth coverage they require With its comprehensive coverage of MPEG video compression, this important book meets the needs of those working to develop the standard as well as those who use MPEG in their work Electrical engineers, multimedia producers, computer scientists, as well as all those interested in this fast growing field will find MPEG Video Compression Standard essential in their work

A watermark for digital images

Abstract: The growth of networked multimedia systems has magnified the need for image copyright protection. One approach used to address this problem is to add an invisible structure to an image that can be used to seal or mark it. These structures are known as digital watermarks. We describe two techniques for the invisible marking of images. We analyze the robustness of the watermarks with respect to linear and nonlinear filtering, and JPEG compression. The results show that our watermarks detect all but the most minute changes to the image.

Rate-distortion optimized mode selection for very low bit rate video coding and the emerging H.263 standard

Abstract: This paper addresses the problem of encoder optimization in a macroblock-based multimode video compression system. An efficient solution is proposed in which, for a given image region, the optimum combination of macroblock modes and the associated mode parameters are jointly selected so as to minimize the overall distortion for a given bit-rate budget. Conditions for optimizing the encoder operation are derived within a rate-constrained product code framework using a Lagrangian formulation. The instantaneous rate of the encoder is controlled by a single Lagrange multiplier that makes the method amenable to mobile wireless networks with time-varying capacity. When rate and distortion dependencies are introduced between adjacent blocks (as is the case when the motion vectors are differentially encoded and/or overlapped block motion compensation is employed), the ensuing encoder complexity is surmounted using dynamic programming. Due to the generic nature of the algorithm, it can be successfully applied to the problem of encoder control in numerous video coding standards, including H.261, MPEG-1, and MPEG-2. Moreover, the strategy is especially relevant for very low bit rate coding over wireless communication channels where the low dimensionality of the images associated with these bit rates makes real-time implementation very feasible. Accordingly, in this paper, the method is successfully applied to the emerging H.263 video coding standard with excellent results at rates as low as 8.0 Kb per second. Direct comparisons with the H.263 test model, TMN5, demonstrate that gains in peak signal-to-noise ratios (PSNR) are achievable over a wide range of rates.

Techniques and Standards for Image, Video, and Audio Coding

Abstract: 1. Introduction. I. DIGITAL CODING TECHNIQUES. 2. Color Formats. 3. Quantization. 4. Predictive Coding. 5. Transform Coding. 6. Hybrid Coding and Motion Compensation. 7. Vector Quantization and Subband Coding. II. INTERNATIONAL STANDARDS FOR IMAGE, VIDEO AND AUDIO CODING. 8. JPEG Still Picture Compression Algorithm. 9. ITU-T H.261 Video Coder. 10. MPEG-1 Audiovisual Coder for Digital Storage Media. 11. MPEG-2 Generic Coding Algorithms. 12. MPEG-4 and H.263 Very Low Bit-rate Coding. 13. High Definition Television Services. 14. CMTT Digital Broadcasting S Standards. Appendix A. Manufactures and Vendors. Appendix B. Information on the Internet.

Phase watermarking of digital images

Abstract: A watermark is an invisible mark placed on an image that can be detected when the image is compared with the original. This mark is designed to identify both the source of an image as well as its intended recipient. The mark should be tolerant to reasonable quality lossy compression of the image using transform coding or vector quantization. Standard image processing operations such as low pass filtering, cropping, translation and rescaling should not remove the mark. Spread spectrum communication techniques and matrix transformations can be used together to design watermarks that are robust to tampering and are visually imperceptible. This paper discusses techniques for embedding such marks in grey scale digital images. It also proposes a novel phase based method of conveying the watermark information. In addition, the use of optimal detectors for watermark identification is also proposed.

Architectures for MPEG compressed bitstream scaling

Abstract: The idea of moving picture expert group (MPEG) bitstream scaling relates to altering or scaling the amount of data in a previously compressed MPEG bitstream. The new scaled bitstream conforms to constraints that are not known nor considered when the original preceded bitstream was constructed. Numerous applications for video transmission and storage are being developed based on the MPEG video coding standard. Applications such as video on demand, trick-play track on digital video tape recorders (VTR's) and extended-play recording on VTR's motivate the idea of bitstream scaling. In this paper, we present several bitstream scaling methods for the purpose of reducing the rate of constant bit rate (CBR) encoded bitstreams. The different methods have varying hardware implementation complexity and associated trade-offs in resulting image quality. Simulation results on MPEG test sequences demonstrate the typical performance trade-offs of the methods.

Content-based video compression

Abstract: A video compression method and system including object-oriented compression plus error correction using decoder feedback.

Entropy estimation of symbol sequences.

Abstract: We discuss algorithms for estimating the Shannon entropy h of finite symbol sequences with long range correlations. In particular, we consider algorithms which estimate h from the code lengths produced by some compression algorithm. Our interest is in describing their convergence with sequence length, assuming no limits for the space and time complexities of the compression algorithms. A scaling law is proposed for extrapolation from finite sample lengths. This is applied to sequences of dynamical systems in non‐trivial chaotic regimes, a 1‐D cellular automaton, and to written English texts.

Image watermarking using DCT domain constraints

Abstract: Watermarking algorithms are used for image copyright protection. The algorithms proposed select certain blocks in the image based on a Gaussian network classifier. The pixel values of the selected blocks are modified such that their discrete cosine transform (DCT) coefficients fulfil a constraint imposed by the watermark code. Two different constraints are considered. The first approach consists of embedding a linear constraint among selected DCT coefficients and the second one defines circular detection regions in the DCT domain. A rule for generating the DCT parameters of distinct watermarks is provided. The watermarks embedded by the proposed algorithms are resistant to JPEG compression.

A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models

Abstract: Describing a video sequence in terms of a small number of coherently moving segments is useful for tasks ranging from video compression to event perception. A promising approach is to view the motion segmentation problem in a mixture estimation framework. However, existing formulations generally use only the motion, data and thus fail to make use of static cues when segmenting the sequence. Furthermore, the number of models is either specified in advance or estimated outside the mixture model framework. In this work we address both of these issues. We show how to add spatial constraints to the mixture formulations and present a variant of the EM algorithm that males use of both the form and the motion constraints. Moreover this algorithm estimates the number of segments given knowledge about the level of model failure expected in the sequence. The algorithm's performance is illustrated on synthetic and real image sequences.

Comparison of video shot boundary detection techniques

Abstract: Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper presents a comparison of several shot boundary detection and classification techniques and their variations including histograms, discrete cosine transform, motion vector, and block matching methods. The performance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with a good mix of transition types. Threshold selection requires a trade-off between recall and precision that must be guided by the target application.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The EREC: an error-resilient technique for coding variable-length blocks of data

Abstract: Many source and data compression schemes work by splitting the input signal into blocks and producing variable-length coded data for each block. If these variable-length blocks are transmitted consecutively, then the resulting coder is highly sensitive to channel errors. Synchronization code words are often used to provide occasional resynchronization at the expense of some added redundant information. This paper introduces the error-resilient entropy code (EREC) as a method for adapting existing schemes to give increased resilience to random and burst errors while maintaining high compression. The EREC has been designed to exhibit graceful degradation with worsening channel conditions. The EREC is applicable to many problems and is particularly effective when the more important information is transmitted near the start of each variable-length block and is not dependent on following data. The EREC has been applied to both still image and video compression schemes, using the discrete cosine transform (DCT) and variable-length coding. The results have been compared to schemes using synchronization code words, and a large improvement in performance for noisy channels has been observed.

Efficient representations of video sequences and their applications

Abstract: Recently, there has been a growing interest in the use of mosaic images to represent the information contained in video sequences. This paper systematically investigates how to go beyond thinking of the mosaic simply as a visualization device, but rather as a basis for an efficient and complete representation of video sequences. We describe two different types of mosaics called the static and the dynamic mosaics that are suitable for different needs and scenarios. These two types of mosaics are unified and generalized in a mosaic representation called the temporal pyramid. To handle sequences containing large variations in image resolution, we develop a multiresolution mosaic. We discuss a series of increasingly complex alignment transformations (ranging from 2D to 3D and layers) for making the mosaics. We describe techniques for the basic elements of the mosaic construction process, namely sequence alignment, sequence integration into a mosaic image, and residual analysis to represent information not captured by the mosaic image. We describe several powerful video applications of mosaic representations including video compression, video enhancement, enhanced visualization, and other applications in video indexing, search, and manipulation.

Applications of universal context modeling to lossless compression of gray-scale images

Abstract: Inspired by theoretical results on universal modeling, a general framework for sequential modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm context. Several variants of the algorithm are described. The sequential, lossless compression schemes obtained when the context modeler is used with an arithmetic coder are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison consistently favoring the proposed approach.

Packet loss resilience of MPEG-2 scalable video coding algorithms

Abstract: Transmission of compressed video over packet networks with nonreliable transport benefits when packet loss resilience is incorporated into the coding. One promising approach to packet loss resilience, particularly for transmission over networks offering dual priorities such as ATM networks, is based on layered coding which uses at least two bitstreams to encode video. The base-layer bitstream, which can be decoded independently to produce a lower quality picture, is transmitted over a high priority channel. The enhancement-layer bitstream(s) contain less information, so that packet losses are more easily tolerated. The MPEG-2 standard provides four methods to produce a layered video bitstream: data partitioning, signal-to-noise ratio scalability, spatial scalability, and temporal scalability. Each was included in the standard in part for motivations other than loss resilience. This paper compares the performance of these techniques (excluding temporal scalability) under various loss rates using realistic length material and discusses their relative merits. Nonlayered MPEG-2 coding gives generally unacceptable video quality for packet loss ratios of 10/sup -3/ for small packet sizes. Better performance can be obtained using layered coding and dual-priority transmission. With data partitioning, cell loss ratios of 10/sup -4/ in the low-priority layer are definitely acceptable, while for SNR scalable encoding, cell loss ratios of 10/sup -3/ are generally invisible. Spatial scalable encoding can provide even better visual quality under packet losses; however, it has a high implementation complexity.

Image compression using wavelet transform and multiresolution decomposition

Abstract: Schemes for image compression of black-and-white images based on the wavelet transform are presented. The multiresolution nature of the discrete wavelet transform is proven as a powerful tool to represent images decomposed along the vertical and horizontal directions using the pyramidal multiresolution scheme. The wavelet transform decomposes the image into a set of subimages called shapes with different resolutions corresponding to different frequency bands. Hence, different allocations are tested, assuming that details at high resolution and diagonal directions are less visible to the human eye. The resultant coefficients are vector quantized (VQ) using the LGB algorithm. By using an error correction method that approximates the reconstructed coefficients quantization error, we minimize distortion for a given compression rate at low computational cost. Several compression techniques are tested. In the first experiment, several 512/spl times/512 images are trained together and common table codes created. Using these tables, the training sequence black-and-white images achieve a compression ratio of 60-65 and a PSNR of 30-33. To investigate the compression on images not part of the training set, many 480/spl times/480 images of uncalibrated faces are trained together and yield global tables code. Images of faces outside the training set are compressed and reconstructed using the resulting tables. The compression ratio is 40; PSNRs are 30-36. Images from the training set have similar compression values and quality. Finally, another compression method based on the end vector bit allocation is examined.

Vector quantization of image subbands: a survey

Abstract: Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system. Vector quantization (VQ) provides a means of converting the decomposed signal into bits in a manner that takes advantage of remaining inter and intraband correlation as well as of the more flexible partitions of higher dimensional vector spaces. Since 1988, a growing body of research has examined the use of VQ for subband/wavelet transform coefficients. We present a survey of these methods.

Method and apparatus for reducing storage requirements for display data

Abstract: A method and apparatus for reducing storage requirements for display data on a computer system (12). Data objects (22) to be displayed are organized into display lists and each data object includes an object type, such as text, graphic, and image. The data objects (22) are rasterized into an uncompressed band buffer and divided into non-intersecting bitmap regions each identified with one or more object types. Each non-empty region is assigned a compression algorithm dependent upon the type of the region and specified compression constraints. The regions are combined with each other into larger regions if appropriate, and each region is compressed using its assigned compression algorithm into a compressed band buffer, thus reducing the required storage space for the data objects. The compressed data is decompressed in scan line order with a selected decompression algorithm corresponding to the assigned compression algorithms to produce uncompressed output data. The uncompressed output data is supplied to an output display device (16) for display.

Digital multi-view video compression with complexity and compatibility constraints

Abstract: In a system and method for transmitting and displaying multiple different views of a scene, three or more simultaneous scene signals, representing multiple different views of a scene, are provided by an appropriate camera arrangement to a spatial multiplexer. The size and resolution of the scene signals are reduced as necessary to combine the multiple scene signals into two super-view signals. The super-view signals are encoded using compression based on redundancies between the two super-views and then transmitted. A decoder receives the transmitted data signal and extracts the two super-view signals according to the inverse of the encoding operation. A spatial demultiplexer recovers the individual scene signals from the decoded super-view signals in accordance with a defined multiplexed order and arrangement. The scene signals are then interpolated as needed to restore the original resolution and size and subsequently displayed.

Rate-Distortion Based Video Compression: Optimal Video Frame Compression and Object Boundary Encoding

Abstract: From the Publisher: The book contains a review chapter on video compression, a background chapter on optimal bit allocation and the necessary mathematical tools, such as the Lagrangian multiplier method and Dynamic Programming. These two introductory chapters make the book self-contained and a fast way of entering this exciting field. Rate-Distortion Based Video Compression establishes a general theory for the optimal bit allocation among dependent quantizers. The minimum total (average) distortion and the minimum maximum distortion cases are discussed. This theory is then used to design efficient motion estimation schemes, video compression schemes and object boundary encoding schemes. For the motion estimation schemes, the theory is used to optimally trade the reduction of energy in the displaced frame difference (DFD) for the increase in the rate required to encode the displacement vector field (DVF). These optimal motion estimators are then used to formulate video compression schemes which achieve an optimal distribution of the available bit rate among DVF, DFD and segmentation. This optimal bit allocation results in very efficient video coders. In the last part of the book, the proposed theory is applied to the optimal encoding of object boundaries, where the bit rate needed to encode a given boundary is traded for the resulting geometrical distortion. Again, the resulting boundary encoding schemes are very efficient. Rate-Distortion Based Video Compression is ideally suited for anyone interested in this booming field of research and development, especially engineers who are concerned with the implementation and design of efficient video compression schemes. It also represents a foundation for future research, since all the key elements needed are collected and presented uniformly. Therefore, it is ideally suited for graduate students and researchers working in this field.

CALIC-a context based adaptive lossless image codec

Abstract: We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.

Lossless compression of VQ index with search-order coding

Abstract: In memoryless vector quantization (VQ) for images, each block is quantized independently and its corresponding index is sent to the decoder. This paper presents a new lossless algorithm that exploits the interblock correlation in the index domain. We compare the current index with previous indices in a predefined search path, and then send the corresponding search order to the decoder. The new algorithm achieves significant reduction of bit rates without introducing extra coding distortion when compared to memoryless VQ. It is very simple and computationally efficient.