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A TECHNIQUE FOR HIGH-PERFORMANCE DATA-compression

COMPUTER 1984年第6期17卷 8-19页

作者： WELCH, TA DIGITAL EQUIPMENT CORP MAYNARDMA 01754 SPERRY RAND CORP RES CTRSUDBURYMA 01776

First Page of the Article

关键词： Data compression Image coding compression algorithms Programming profession Data structures Encoding Runtime System performance

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Scalable lossless compression algorithm for motion pictures

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1998年第2期44卷 333-336页

作者： Wakatani, A Matsushita Elect Ind Co Ltd Corp Res Div Osaka Japan

Lossless JPEG is inherently a sequential algorithm. In order to achieve realtime encode/decode of lossless JPEG for motion pictures, it should be parallelized. We show the parallelized version of lossless JPEG and the allocation of virtual processors. We also describe the preliminary performance prediction of the algorithm to confirm the possibility of the realtime processing.

关键词： compression algorithms Motion pictures Transform coding Image coding Concurrent computing Decoding Biomedical imaging Image segmentation Discrete cosine transforms Motion estimation

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Efficient compression for Remote Sensing: Multispectral Transform and Deep Recurrent Neural Networks for Lossless Hyper-Spectral Imagine

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第2期15卷 531-541页

作者： Anuradha, D. Sekhar, Gillala Chandra Mishra, Annapurna Thapar, Puneet El-Ebiary, Yousef A. Baker Syamala, Maganti Panimalar Engn Coll Dept CSBS Chennai Tamil Nadu India Inst Aeronaut Engn Dept Comp Sci & Engn Hyderabad 500043 India Silicon Inst Technol Dept Elect & Commun Engn Bhubaneswar 751024 India Lovely Profess Univ Comp Sci & Engn Dept Phagwara Punjab India UniSZA Univ Fac Informat & Comp Kuala Terengganu Malaysia Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 Andhra Pradesh India

Remote sensing technologies, which are essential for everything from environmental monitoring to disaster relief, enable large-scale multispectral data collection. In the field of hyper-spectral imaging, where high-dimensional data is required for precise analysis, effective compression techniques are critical for transmission and storage. In the field of hyper-spectral imaging, the development of efficient compression techniques is critical because datasets containing high-dimensional information must be transmitted and stored efficiently without sacrificing analytical precision. The paper presents advanced compression techniques that combine deep Recurrent Neural Networks (RNNs) with multispectral transforms to achieve lossless compression in hyper-spectral imaging. The Discrete Wavelet Transform (DWT) is used to efficiently capture spectral and spatial information by utilizing the properties of multispectral transforms. Simultaneously, deep RNNs are used to model the hyper-spectral data with complex dependencies, allowing for sequential compression. The overall compression efficiency that is increased by the integration of spatial and spectral information allows for reduced storage requirements and improved transmission efficiency. Python software is used to implement the proposed model. When compared to Liner Spectral Mixture Analysis (LSMA) based compression, Spatial Orientation Tree Wavelet (STW)-Wavelet Difference Reduction (WDR), and DPCM, the proposed DWT-RNN-LSTM method has a better PSNR value of 45 dB and a lower MSE of 7.50%. Adaptive compression methods are presented in order to dynamically adapt to various data properties and ensure application in various hyperspectral scenes. Studies on hyper-spectral images of various sizes and resolutions demonstrate the approach's scalability and generalization, as well as the utility and adaptability of the proposed compression framework in a variety of remote sensing scenarios.

关键词： Multi-Spectral transform lossless compression hyper-spectral data deep recurrent neural network compression algorithms

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Compaction of RFID Devices Using Data compression

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2017年第3期1卷 202-207页

作者： Wiseman, Yair Bar Ilan Univ Comp Sci Dept IL-52900 Ramat Gan Israel

When a coding system used to represent letters or numbers in transmitting messages has many unused codewords, it will be more than reasonable to compress the messages. Electronic product code (EPC) which is widely used by many RFID devices has plentiful unused codewords. As a matter of fact, the unused codewords are most by far of the codewords. In this paper, we check the suitableness of a commonly used compression algorithm for EPC of RFID devices.

关键词： Huffman coding RFID tags compression algorithms Product codes Compaction Data compression RFID electronic product code (EPC) compression arithmetic coding

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Numerical compression Schemes for Proteomics Mass Spectrometry Data

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MOLECULAR & CELLULAR PROTEOMICS 2014年第6期13卷 1537-1542页

作者： Teleman, Johan Dowsey, Andrew W. Gonzalez-Galarza, Faviel F. Perkins, Simon Pratt, Brian Roest, Hannes L. Malmstroem, Lars Malmstrom, Johan Jones, Andrew R. Deutsch, Eric W. Levander, Fredrik Lund Univ Dept Immunotechnol S-22381 Lund Sweden Univ Manchester Fac Med & Human Sci Inst Human Dev Manchester M13 9WL Lancs England Univ Manchester CADET Manchester M13 9WL Lancs England Cent Manchester Univ Hosp NHS Fdn Trust Manchester Acad Hlth Sci Ctr Manchester M13 9WL Lancs England Univ Liverpool Inst Integrat Biol Liverpool L69 7ZB Merseyside England Univ Washington Dept Genome Sci Sch Med Seattle WA 98195 USA ETH Dept Biol Inst Mol Syst Biol CH-8093 Zurich Switzerland Lund Univ Fac Med Dept Clin Sci SE-22184 Lund Sweden Inst Syst Biol Seattle WA 98109 USA Lund Univ S-22100 Lund Sweden

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naive mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

关键词： Vendors Data Files compression algorithms binary data development platform read time proteomics Mass spectrometers Quadruple

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ANALYSIS OF SOME REDUNDANCY REMOVAL BANDWIDTH compression TECHNIQUES

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PROCEEDINGS OF THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 1967年第3期55卷 278-&页

作者： EHRMAN, L SIGNATRON Inc. Lexington MA USA

Three redundancy removal bandwidth compression algorithms --the floating-aperture predictor, the zero-order interpolator, and the fan interpolator--are analyzed. Theoretical expressions are found for the mean and mean-square times between output samples of these devices when the input signal is a Markov process. These expressions are evaluated for the case in which the input is a first-order Gaussian Markov process, and the resulting output sampling rates and transmission bandwidths are compared to those required by a PCM system using uniform sampling and optimum linear filter interpolation. It is shown that, given sufficient a priori knowledge of the signal process, there is little to be gained by using these redundancy removal techniques in place of the PCM system. However, if the signal statistics are unknown, the use of these algorithms instead of PCM may provide a considerable bandwidth reduction.

关键词： Bandwidth Phase change materials Signal processing Markov processes Signal sampling compression algorithms Algorithm design and analysis Nonlinear filters Interpolation Statistics

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Poisson recoding of solar images for enhanced compression

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SOLAR PHYSICS 2005年第1-2期228卷 253-264页

作者： Nicula, B Berghmans, D Hochedez, JF Royal Observ Belgium B-1180 Brussels Belgium

The amount of useful scientific data that a space-borne telescope produces is often limited by the available telemetry of the platform. General purpose image compression schemes are usually used to compress the image either lossy or losslessly. These schemes do not take into account the fact that pixel values of typical solar images are only known to within a certain uncertainty range. We present a preprocessing method to enhance the performance (compression ratio) of any subsequent image compression scheme. The method uses estimates of the photon shot and thermal noises to compute a recoding look-up table that maps the initial data into uncertainty intervals. The recoding method is lossy in a mathematical sense but lossless in a physical sense, since the image alterations are guaranteed to be smaller than the Poisson noise. The performance of any further compression algorithm is enhanced while achieving a known guaranteed maximum absolute error for each pixel in the case of lossless compression.

关键词： RAW DATA compression algorithms Image compression Scheme Lossless pixel Sim\'eon Denis Poisson Poisson noise Data compression Thermal noise lossless compression

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Electrocardiogram compression using the nonlinear iterative partial least squares algorithm: a comparison between adaptive and non-adaptive approach

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2020年第4期33卷 367-385页

作者： Ricchetti, Pier Marco Nicolosi, Denys E. C. USP Univ Dante Pazzanese Inst Cardiol Sch Med Ave Dr Dante Pazzanese500 Vila Mariana BR-04012909 Sao Paulo SP Brazil Fundacao Educ Inaciana FEI Ave Humberto Castelo Branco3972 Assuncao BR-09850901 Sao Bernardo Do Campo SP Brazil Sao Judas Univ Rua Taquari546 Mooca BR-03166000 Sao Paulo SP Brazil

Data compression is applicable in reducing amount of data to be stored and it can be applied in several data collecting processes, being generated by lossy or lossless compression algorithms. Due to its large amount of data, the use of compression is desirable in ECG signals. In this work, we present the accepted nonlinear iterative partial least squares (NIPALS) method as an option to ECG compression method, as recommended by Nicolosi (1999). In addition, we compare the results based in an adaptive and non-adaptive version of this method, by using the MIT arrhythmia database. As a help to obtain a better comparison, we have developed an abnormality indicator related to possible abnormalities in the waveform and a decision method that helps to choose between adaptive or non-adaptive approach. Results showed that the adaptive approach is better than the non-adaptive approach, for the NIPALS compression algorithm.

关键词： component analysis adaptive comparison principal component analysis PCA NIPALS electrocardiogram ECG compression algorithms data compression

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Graph automorphisms for compression

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OPEN COMPUTER SCIENCE 2020年第1期11卷 51-59页

作者： Cibej, Uros Mihelic, Jurij Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia

Detecting automorphisms is a natural way to identify redundant information presented in structured data. When such redundancies are detected they can be used for data compression. In this paper we explore two different classes of graphs to capture this intuitive property of automorphisms. Symmetry-compressible graphs are the first class which introduces the basic concepts but use only global symmetries for the compression. In order for this concept to be more practical, we need to use local symmetries. Thus, we extend the basic graph class with Near Symmetry compressible graphs. Furthermore, we develop two algorithms that can be used to compress practical instances and empirically evaluate them on a set of realistic graphs.

关键词： graph automorphisms graph classes compression algorithms

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Sequential Change Detection Based on Universal compression for Markov Sources

Sequential Change Detection Based on Universal Compression f...

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IEEE International Symposium on Information Theory (ISIT)

作者： Verma, A. Bansal, R. K. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92103 USA Indian Inst Technol Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Uttar Pradesh India

ISBN: (纸本)9781538692912

A universal compression code can act as an estimator for the distribution of a finite alphabet finite-order Markov source. This property of universal codes was exploited by Jacob and Bansal in [5] to propose a modification of the CUSUM test in order to solve the change detection problem when the post-change distribution is not known. The performance of this test was proven to be asymptotically optimal for a memoryless sources and class of sources with memory under Lorden's minimax formulation. This study was further extended in [9] where performance of the modified CUSUM for an i.i.d. setting under Lai's criterion involving a constraint on the probability of false alarm within a window (PFAW) was analyzed. In this paper, we introduce a modified version of the window limited CUSUM (WL-CUSUM) test by incorporating strongly universal code. We closely follow the work of Lai [8] in order to prove the asymptotic optimality for the test under the PFAW criterion. We further prove the asymptotic optimality of the modified WL-CUSUM test in the Bayesian setting.

关键词： Markov processes Delays compression algorithms Jacobian matrices Performance analysis Microsoft Windows Bayes methods

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