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LAMP: Improving compression Ratio for AMR Applications via Level Associated Mapping-Based Preconditioning

IEEE TRANSACTIONS ON COMPUTERS 2023年第12期72卷 3370-3382页

作者： Li, Yida Luo, Huizhang Li, Fenfang Wang, Junqi Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Beijing Inst Gen Artificial Intelligence BIGAI Beijing 100080 Peoples R China

Data compression can efficiently reduce the memory and persistence storage cost, which is highly desirable in modern computing systems, such as enterprise, cloud, and High-Performance Computing (HPC) environments. However, the main challenges of existing data compressors are the insufficient compression ratio and low throughput. This paper focuses on improving the compression ratio of state-of-the-art lossy compression algorithms from the view of applications. Besides, we also use the characteristics of the applications to reduce the runtime overhead. To this end, we explore the idea with Adaptive Mesh Refinement (AMR), which is widely adopted as a computational technique to reduce the amount of computation and memory required in scientific simulations. We propose Level Associated Mapping-based Preconditioning (LAMP) to improve the storage efficiency of AMR applications. The main idea is twofold. First, we utilize the high similarities among the adjacent AMR levels to precondition the data prior to compression. Second, AMR has a unique characteristic of grid structures. We utilize grid structures to rebuild a level associated mapping table, which significantly reduces the runtime overhead of LAMP. Thanks to the optimization techniques of General Matrix Multiplication (GEMM), we further accelerate the process of rebuilding AMR hierarchy for LAMP. Besides, we also block multiple adjacent coordinates within a box and further improve cache locality. The experimental results show that the compression ratios of LAMP are improved up to 63.8% compared to directly compressing the data.

关键词： Runtime Data compression compression algorithms Adaptive mesh refinement Predictive models Optimization Compressors high-performance computing (HPC) adaptive mesh refinement (AMR) data preconditioning AMR level associated mapping cache locality

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A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients

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DIGITAL SIGNAL PROCESSING 2003年第4期13卷 604-622页

作者： Al-Shrouf, A Abo-Zahhad, M Ahmed, SM Yarmouk Univ Hijjawi Fac Engn & Technol Dept Elect Engn Irbid Jordan Appl Sci Univ Fac Engn Dept Elect & Comp Engn Amman Jordan Univ Assiut Fac Engn Dept Elect & Elect Engn Assiut Egypt

This paper describes a new algorithm for electrocardiogram (ECG) compression. The main goal of the algorithm is to reduce the bit rate while keeping the reconstructed signal distortion at a clinically acceptable level. It is based on the compression of the linearly predicted residuals of the wavelet coefficients of the signal. In this algorithm, the input signal is divided into blocks and each block goes through a discrete wavelet transform;then the resulting wavelet coefficients are linearly predicted. In this way, a set of uncorrelated transform domain signals is obtained. These signals are compressed using various coding methods, including modified run-length and Huffman coding techniques. The error corresponding to the difference between the wavelet coefficients and the predicted coefficients is minimized in order to get the best predictor. The method is assessed through the use of percent root-mean square difference (PRD) and visual inspection measures. By this compression method, small PRD and high compression ratio with low implementation complexity are achieved. Finally, we have compared the performance of the ECG compression algorithm on data from the MIT-BIH database. (C) 2003 Elsevier Inc. All rights reserved.

关键词： ECG LPC wavelet transform compression algorithms

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compression of skin tumor images

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 1998年第3期17卷 73-80页

作者： Kjoelen, A Umbaugh, SE Zuke, M So Illinois Univ Dept Elect Engn Comp Vis & Image Proc Lab Edwardsville IL 62026 USA

The following topics are dealt with: error measures; transform-domain processing; preprocessing of color images; the principal components transform; the discrete wavelet transform; wavelet decomposition and reconstruction; vector quantization; distortion measures; a codebook design algorithm; design and implementation of compression algorithms; preprocessing effects; quality assessment of the compressed images.

关键词： Image coding Skin neoplasms Discrete wavelet transforms Distortion measurement Algorithm design and analysis Color Image reconstruction Vector quantization compression algorithms Quality assessment

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Electrocardiogram compression technique for global system of mobile-based offline telecardiology application for rural clinics in India

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2012年第6期6卷 412-419页

作者： Mitra, M. Bera, J. N. Gupta, R. Univ Calcutta Dept Appl Phys Kolkata 70009 WB India

compression of Electrocardiographic (ECG) data is an important requirement to develop an efficient telecardiology application. This study describes an offline compression technique, which is implemented for ECG transmission in a global system of mobile (GSM) network for preliminary level evaluation of patient's cardiac condition in a non-critical condition. A short-duration (5-6 beats) ECG data from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database is used for the trial. The compression algorithm is based on direct processing of ECG samples in four major steps: viz., down-sampling of dataset, normalising inter-sample differences, grouping for sign and magnitude encoding, zero element compression and finally, conversion of bytes into corresponding 8 bit American standard code for information interchange (ASCII) characters. The developed software at the patient side computer also converts the compressed data file into formatted sequence of short text messages (SMSs). Using a dedicated GSM module these message are delivered to the mobile phone of the remote cardiologist. The received SMSs are to be downloaded at the authors computer for concatenation and decompression to obtain back the original ECG for visual or automated investigation. An average percentage root-mean-squared difference and compression ratio values of 43.54 and 1.73 are obtained, respectively, with MIT-BIH arrhythmia data. The proposed technique is useful for rural clinics in India for preliminary level cardiac investigation.

关键词： electrochemical grinding clinic Byte compression telecardiology OFF LINE direct processing compression algorithms

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A Spectral-Spatial Feature Extraction Method With Polydirectional CNN for Multispectral Image compression

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2745-2758页

作者： Kong, Fanqiang Hu, Kedi Li, Yunsong Li, Dan Liu, Xin Durrani, Tariq S. Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland

Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN.

关键词： Feature extraction Image coding Transform coding Convolution Decoding Correlation Convolutional neural networks compression algorithms convolutional neural network (CNN) feature extraction multispectral image rate-distortion optimizer

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compression OF THE AMBULATORY ECG BY AVERAGE BEAT SUBTRACTION AND RESIDUAL DIFFERENCING

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1991年第3期38卷 253-259页

作者： HAMILTON, PS TOMPKINS, WJ UNIV WISCONSIN DEPT ELECT ENGNMADISONWI 53706

We implemented a method for compression of the abulatory ECG that includes average beat subtraction and first differencing of residual data. Our previous investigations indicated that this method is superior to other compression methods with respect to data rate as a function mean-squared-error distortion. Based on previous results we selected a sample rate of 100 samples per second and a quantization step size of 35-mu-V. These selections allow storage of 24 h of two-channel ECG data in 4 Mbytes of memory with a minimum rms distortion. For this sample rate and quantization level, we show that estimation of beat location and quantizer location can significantly affect compression performance. Improved compression resulted when beats were located with a temporal resolution of 5 ms and coarse quantization was performed in the compression loop. For the 24-h MIT/BIH arrhythmia database our compression algorithm coded a single-channel of ECG data with an average data rate of 174 bits per second.

关键词： Electrocardiography Databases compression algorithms Quantization Data compression Predictive coding Real time systems Computational efficiency Prediction algorithms Huffman coding

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Hybrid-Smash: A Heterogeneous CPU-GPU compression Library

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IEEE ACCESS 2024年 12卷 32706-32723页

作者： Penaranda, Cristian Reano, Carlos Silla, Federico Univ Politecn Valencia DISCA Valencia 46022 Spain Univ Valencia Dept Informat ETSE Burjassot 46100 Spain

compression algorithms are widely used to reduce data size and improve application performance. Nevertheless, data compression has a computational cost which can limit its use. GPUs could be leveraged to reduce compression time. However, existing GPU-based compression libraries expect data to compress in GPU memory, although it is usually stored in CPU memory. Additionally, setup time of GPUs could be a problem when compressing small data sizes. In this paper, we implement a new GPU-based compression library. Contrary to existing ones, our library uses data located in CPU memory. Performance results show that, for the same compression algorithms, GPUs are beneficial for larger data sizes whereas smaller data sizes are compressed faster using CPUs. Therefore, we enhance our proposal with Hybrid-Smash: a heterogeneous CPU-GPU compression library, which transparently uses CPU or GPU compression depending on data size, thus improving compression for any data size.

关键词： Graphics processing units Image coding compression algorithms Symbols Proposals Artificial intelligence Parallel processing Performance evaluation Central Processing Unit Hybrid intelligent systems Data systems Size control Lossless compression parallel computing GPU

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An In-Sight Into How compression Dictionary Architecture Can Affect the Overall Performance in FPGAs

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IEEE ACCESS 2020年 8卷 183101-183116页

作者： Bartik, Matej Benes, Tomas Kubalik, Pavel Czech Tech Univ Fac Informat Technol Dept Digital Design Prague 16000 Czech Republic CESNET Dept Technol Network Applicat Prague 16000 Czech Republic

This paper presents a detailed analysis of various approaches to hardware implemented compression algorithm dictionaries, including our optimized method. To obtain comprehensive and detailed results, we introduced a method for the fair comparison of programmable hardware architectures to show the benefits of our approach from the perspective of logic resources, frequency, and latency. We compared two generally used methods with our optimized method, which was found to be more suitable for maintaining the memory content via (in)valid bits in any mid-density memory structures, which are implemented in programmable hardware such as FPGAs (Field Programmable Gate Array). The benefits of our new method based on a x201C;Distributed Memoryx201D;technique are shown on a particular example of compression dictionary but the method is also suitable for another use cases requiring a fast (re-)initialization of the used memory structures before each run of an algorithm with minimum time and logic resources consumption. The performance evaluation of the respective approaches has been made in Xilinx ISE and Xilinx Vivado toolkits for the Virtex-7 FPGA family. However the proposed approach is compatible with 99x0025;of modern FPGAs.

关键词： Dictionaries Field programmable gate arrays compression algorithms Computer architecture Random access memory Throughput IP networks compression algorithm compression dictionary FPGA hash table LZ4 LZ77 memory architecture performance comparison status register

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FIXED DATA-BASE VERSION OF THE LEMPEL-ZIV DATA-compression ALGORITHM

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IEEE TRANSACTIONS ON INFORMATION THEORY 1991年第3期37卷 878-890页

作者： WYNER, AD ZIV, J TECHNION ISRAEL INST TECHNOL FAC ELECT ENGNIL-32000 HAIFAISRAEL

It is demonstrated that a variant of the Lempel-Ziv data compression algorithm where the data base is held fixed and is reused to encode successive strings of incoming input symbols is optimal, provided that the sourc... 详细信息

关键词： Data compression Decoding Entropy Encoding compression algorithms Databases Binary sequences Statistics

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Large Database compression Based on Perceived Information

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1735-1739页

作者： Maugey, Thomas Toni, Laura Campus Univ Beaulieu Rennes Bretagne Atlantique F-35042 Rennes France UCL Elect & Elect Engn Dept London WC1E 7JE England

Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the information loss perceived by the final user instead of the actual source quality loss, under compression rate constraints. As main contributions, we first introduce the concept of perceived information (PI), which reflects the information perceived by a given user experiencing a data collection, and which is evaluated as the volume spanned by the sources features in a personalized latent space. We then formalize the rate-PI optimization problem and propose an algorithm to solve this compression problem. Finally, we validate our algorithm against benchmark solutions with simulation results, showing the gain in taking into account users' preferences while also maximizing the perceived information in the feature domain.

关键词： Covariance matrices compression algorithms Databases Measurement Signal processing algorithms Image coding Entropy Data compression large database repurposing sampling

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