Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

High-performance compression of visual information - A tutorial review - Part I : Still Pictures

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must be defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare stateof- the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated

Efficient Index-based Methods for Processing Large Biological Databases.

Over the last few decades, advances in life sciences have generated a vast amount of biological data. To cope with the rapid increase in data volume, there is a pressing need for efficient computational methods to query large biological datasets. This thesis develops efficient and scalable querying methods for biological data. For an efficient sequence database search, we developed two q-gram index based algorithms, miBLAST and ProbeMatch. miBLAST is designed to expedite batch identification of statistically significant sequence alignments. ProbeMatch is designed for identifying sequence alignments based on a k-mismatch model. For an efficient protein structure database search, we also developed a multi-dimensional index based algorithm method called proCC, an automatic and efficient classification framework. All these algorithms result in substantial performance improvements over existing methods. When designing index-based methods, the right choice of indexing methods is essential. In addition to developing index-based methods for biological applications, we also investigated an essential database problem that reexamines the state-of-the-art indexing methods by experimental evaluation. Our experimental study provides a valuable insight for choosing the right indexing method and also motivates a careful consideration of index structures when designing index-based methods. In the long run, index-based methods can lead to new and more efficient algorithms for querying and mining biological datasets. The examples above, which include query processing on biological sequence and geometrical structure datasets, employ index-based methods very effectively. While the database research community has long recognized the need for index-based query processing algorithms, the bioinformatics community has been slow to adopt such algorithms. However, since many biological datasets are growing very rapidly, database-style index-based algorithms are likely to play a crucial role in modern bioinformatics methods. The work proposed in this thesis lays the foundation for such methods.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61570/1/youjkim_1.pd

Three dimensional DCT based video compression.

by Chan Kwong Wing Raymond.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 115-123).Acknowledgments --- p.iTable of Contents --- p.ii-vList of Tables --- p.viList of Figures --- p.viiAbstract --- p.1Chapter Chapter 1 : --- IntroductionChapter 1.1 --- An Introduction to Video Compression --- p.3Chapter 1.2 --- Overview of Problems --- p.4Chapter 1.2.1 --- Analog Video and Digital Problems --- p.4Chapter 1.2.2 --- Low Bit Rate Application Problems --- p.4Chapter 1.2.3 --- Real Time Video Compression Problems --- p.5Chapter 1.2.4 --- Source Coding and Channel Coding Problems --- p.6Chapter 1.2.5 --- Bit-rate and Quality Problems --- p.7Chapter 1.3 --- Organization of the Thesis --- p.7Chapter Chapter 2 : --- Background and Related WorkChapter 2.1 --- Introduction --- p.9Chapter 2.1.1 --- Analog Video --- p.9Chapter 2.1.2 --- Digital Video --- p.10Chapter 2.1.3 --- Color Theory --- p.10Chapter 2.2 --- Video Coding --- p.12Chapter 2.2.1 --- Predictive Coding --- p.12Chapter 2.2.2 --- Vector Quantization --- p.12Chapter 2.2.3 --- Subband Coding --- p.13Chapter 2.2.4 --- Transform Coding --- p.14Chapter 2.2.5 --- Hybrid Coding --- p.14Chapter 2.3 --- Transform Coding --- p.15Chapter 2.3.1 --- Discrete Cosine Transform --- p.16Chapter 2.3.1.1 --- 1-D Fast Algorithms --- p.16Chapter 2.3.1.2 --- 2-D Fast Algorithms --- p.17Chapter 2.3.1.3 --- Multidimensional DCT Algorithms --- p.17Chapter 2.3.2 --- Quantization --- p.18Chapter 2.3.3 --- Entropy Coding --- p.18Chapter 2.3.3.1 --- Huffman Coding --- p.19Chapter 2.3.3.2 --- Arithmetic Coding --- p.19Chapter Chapter 3 : --- Existing Compression SchemeChapter 3.1 --- Introduction --- p.20Chapter 3.2 --- Motion JPEG --- p.20Chapter 3.3 --- MPEG --- p.20Chapter 3.4 --- H.261 --- p.22Chapter 3.5 --- Other Techniques --- p.23Chapter 3.5.1 --- Fractals --- p.23Chapter 3.5.2 --- Wavelets --- p.23Chapter 3.6 --- Proposed Solution --- p.24Chapter 3.7 --- Summary --- p.25Chapter Chapter 4 : --- Fast 3D-DCT AlgorithmsChapter 4.1 --- Introduction --- p.27Chapter 4.1.1 --- Motivation --- p.27Chapter 4.1.2 --- Potentials of 3D DCT --- p.28Chapter 4.2 --- Three Dimensional Discrete Cosine Transform (3D-DCT) --- p.29Chapter 4.2.1 --- Inverse 3D-DCT --- p.29Chapter 4.2.2 --- Forward 3D-DCT --- p.30Chapter 4.3 --- 3-D FCT (3-D Fast Cosine Transform Algorithm --- p.30Chapter 4.3.1 --- Partitioning and Rearrangement of Data Cube --- p.30Chapter 4.3.1.1 --- Spatio-temporal Data Cube --- p.30Chapter 4.3.1.2 --- Spatio-temporal Transform Domain Cube --- p.31Chapter 4.3.1.3 --- Coefficient Matrices --- p.31Chapter 4.3.2 --- 3-D Inverse Fast Cosine Transform (3-D IFCT) --- p.32Chapter 4.3.2.1 --- Matrix Representations --- p.32Chapter 4.3.2.2 --- Simplification of the calculation steps --- p.33Chapter 4.3.3 --- 3-D Forward Fast Cosine Transform (3-D FCT) --- p.35Chapter 4.3.3.1 --- Decomposition --- p.35Chapter 4.3.3.2 --- Reconstruction --- p.36Chapter 4.4 --- The Fast Algorithm --- p.36Chapter 4.5 --- Example using 4x4x4 IFCT --- p.38Chapter 4.6 --- Complexity Comparison --- p.43Chapter 4.6.1 --- Complexity of Multiplications --- p.43Chapter 4.6.2 --- Complexity of Additions --- p.43Chapter 4.7 --- Implementation Issues --- p.44Chapter 4.8 --- Summary --- p.46Chapter Chapter 5 : --- QuantizationChapter 5.1 --- Introduction --- p.49Chapter 5.2 --- Dynamic Ranges of 3D-DCT Coefficients --- p.49Chapter 5.3 --- Distribution of 3D-DCT AC Coefficients --- p.54Chapter 5.4 --- Quantization Volume --- p.55Chapter 5.4.1 --- Shifted Complement Hyperboloid --- p.55Chapter 5.4.2 --- Quantization Volume --- p.58Chapter 5.5 --- Scan Order for Quantized 3D-DCT Coefficients --- p.59Chapter 5.6 --- Finding Parameter Values --- p.60Chapter 5.7 --- Experimental Results from Using the Proposed Quantization Values --- p.65Chapter 5.8 --- Summary --- p.66Chapter Chapter 6 : --- Entropy CodingChapter 6.1 --- Introduction --- p.69Chapter 6.1.1 --- Huffman Coding --- p.69Chapter 6.1.2 --- Arithmetic Coding --- p.71Chapter 6.2 --- Zero Run-Length Encoding --- p.73Chapter 6.2.1 --- Variable Length Coding in JPEG --- p.74Chapter 6.2.1.1 --- Coding of the DC Coefficients --- p.74Chapter 6.2.1.2 --- Coding of the DC Coefficients --- p.75Chapter 6.2.2 --- Run-Level Encoding of the Quantized 3D-DCT Coefficients --- p.76Chapter 6.3 --- Frequency Analysis of the Run-Length Patterns --- p.76Chapter 6.3.1 --- The Frequency Distributions of the DC Coefficients --- p.77Chapter 6.3.2 --- The Frequency Distributions of the DC Coefficients --- p.77Chapter 6.4 --- Huffman Table Design --- p.84Chapter 6.4.1 --- DC Huffman Table --- p.84Chapter 6.4.2 --- AC Huffman Table --- p.85Chapter 6.5 --- Implementation Issue --- p.85Chapter 6.5.1 --- Get Category --- p.85Chapter 6.5.2 --- Huffman Encode --- p.86Chapter 6.5.3 --- Huffman Decode --- p.86Chapter 6.5.4 --- PutBits --- p.88Chapter 6.5.5 --- GetBits --- p.90Chapter Chapter 7 : --- "Contributions, Concluding Remarks and Future Work"Chapter 7.1 --- Contributions --- p.92Chapter 7.2 --- Concluding Remarks --- p.93Chapter 7.2.1 --- The Advantages of 3D DCT codec --- p.94Chapter 7.2.2 --- Experimental Results --- p.95Chapter 7.1 --- Future Work --- p.95Chapter 7.2.1 --- Integer Discrete Cosine Transform Algorithms --- p.95Chapter 7.2.2 --- Adaptive Quantization Volume --- p.96Chapter 7.2.3 --- Adaptive Huffman Tables --- p.96Appendices:Appendix A : The detailed steps in the simplification of Equation 4.29 --- p.98Appendix B : The program Listing of the Fast DCT Algorithms --- p.101Appendix C : Tables to Illustrate the Reording of the Quantized Coefficients --- p.110Appendix D : Sample Values of the Quantization Volume --- p.111Appendix E : A 16-bit VLC table for AC Run-Level Pairs --- p.113References --- p.11