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Data compression Conference (DCC)

作者： Rui Chen Lihao Xu Mathworks Inc. Natick MA Wayne State University Detroit MI

LZ4 data compression algorithm is the current state-of-art compression algorithm in the high speed compression algorithm class, and has been adopted and integrated by lots modern high-speed data storage systems. We propose a fast lossless compression algorithm, named LZ4r. A new format of the data sequence is designed, and by integrating it into the proposed algorithm, a better compression ratio than LZ4 is achieved. Numerous evaluation tests are conducted with different sets of data corpus. The results consistently show that LZ4r gains a significant improvement in compression ratio than LZ4, with a similar high compression speed. Although LZ4r is slower than LZ4 in decompression speed, the decompression speed of LZ4r is still fast enough not to reduce the overall performance of the system. Thus, LZ4r can become a practical and competitive alternative or replacement of LZ4 in many high-speed data storage systems to improve the overall performance and lower the overall cost. More details about LZ4r algorithm design and performance evaluation can be found at [1].

关键词： Performance evaluation Costs Buffer storage Data compression compression algorithms

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The Study of compression algorithms for WebSocket Protocol

The Study of Compression Algorithms for WebSocket Protocol

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Tanapat Anusas-amornkul Chanapa Silawong Department of Computer and Information Science King Mongkut's University of Technology North Bangkok

ISBN: (纸本)9781479929948

WebSocket protocol is a part of HTML5 standard, which is suitable for real-time and full duplex web applications such as online chat, notification server, and online game systems. A compression algorithm for WebSocket protocol has yet to be defined as a standard. There is a proposal to use Deflate algorithm, a lossless compression algorithm for WebSocket, but other compression algorithms are also available. In this paper, Deflate algorithm is compared with other lossless algorithms, i.e. Huffman, LZ77 and LZW algorithms in order to find a suitable compression algorithm for WebSocket protocol. A compression library is developed using JavaScript for studying and analyzing such compression algorithms for different data formats. In addition, an instant messaging system and an online game are developed and studied as the examples of WebSocket real-time and full duplex web applications. The performance metrics for this study are a compression ratio, compression and decompression speeds and overall time. In our experiments, the deflate compression algorithm gains the best result in terms of overall time and compression ratio for WebSocket protocol.

关键词： Component WebSocket protocol compression algorithms Huffman LZ77 LZW and Deflate

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Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第28期34卷 e7383-e7383页

作者： Kumar, S. Suresh Sarankumar, R. Vignesh, O. Prakash, A. QIS Coll Engn & Technol Dept Elect & Commun Engn Ongole Andhra Pradesh India Easwari Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India QIS Coll Engn & Technol Dept Elect & Elect Engn Ongole Andhra Pradesh India

A highly challenging aspect of the data compression technique is maintaining the quality of data that reconstructs in high compression rates. To overcome these limitations, a bilateral anisotropic Gabor wavelet transformation with deep stacked auto encoding (BAGWT-DSAE) technique based lossesless image compression is proposed in this article to save the storage space and processing time during transferring the images. The proposed method contains three main processes namely preprocessing, compression and decompression. Initially input aerial image and digital image are taken and these images are given bilateral filter based preprocessing for eliminates the different types of noises and also multiple artifacts. Then the preprocessed images are given to anisotropic Gabor wavelet transformation based deep stacked auto encoding to compress and decompress the wavelet transform's sensitive sub-bands effectually. In DSAE, the decoder of the auto encoder achieves a better quality decompressed image. The proposed method is implemented in MATLAB simulations run in PC through Intel Core, 8 GB of RAM, 2.50 GHz CPU and Windows 8. Then, the simulation performance of proposed BAGWT-DSAE-LIC method provides 20.23%, 24.85%, and 38.56% low compression ratio and 26.48%, 21.23%, and 12.53% lower computational time, 4.56%, 7.68%, and 8.34% high space saving than the existing methods.

关键词： bilateral anisotropic Gabor wavelet transformation bilateral filter compression algorithms compression rate deep stacked auto encoding

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Measuring the Similarity of Files by Data compression

Measuring the Similarity of Files by Data Compression

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Data compression Conference (DCC)

作者： Hubert Schölnast Institute of IT Security Research St.Pölten University of Applied Sciences Austria

The two meta-algorithms Concat Compress and Cross Compress, which can be used to measure the similarity of files, were subjected to an extensive practical test together with the compression algorithms Re-Pair, gzip and bz2:Five labeled datasets with 6533 entries and approximately 10 MB were subjected to a classification procedure using these algorithms. Theoretical considerations of the two meta-algorithms have been made in the past [1], but the practical implementation of these methods is still in its infancy. The results from our experiments are promising and show the great potential of this approach.

关键词： Data compression Approximation algorithms Classification algorithms compression algorithms

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Improving Genome compression Performance by Extending Reference Sequences

Improving Genome Compression Performance by Extending Refere...

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Intelligent Supercomputing and BioPharma (ISBP), International Conference on

作者： XiangDong Ma Jianhua Chen School of Information Science and Engineering Yunnan University Kunming China

We propose an efficient referential genome compression algorithm called RCCG. It extends reference genomes by its reverse complementation and uses coprime window sampling to detect the maximum matches (MEMs) between two genome sequences. After the assessment, those selected matches will be united to form mutation-containing matches (MCMs). The average compression ratio of the proposed algorithm is higher than that of the state-of-the-art genome compression algorithms.

关键词： Micromechanical devices Genomics Bioinformatics compression algorithms

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Analysis of Lossless Compressors Applied to Integer and Floating-Point Astronomical Data

Analysis of Lossless Compressors Applied to Integer and Floa...

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Data compression Conference (DCC)

作者： Maireles-Gonzalez, Oscar Bartrina-Rapesta, Joan Hernandez-Cabronero, Miguel Serra-Sagrista, Joan Univ Autonoma Barcelona Cerdanyola Valles Bellaterra 08193 Spain

ISBN: (纸本)9781665478939

In this work, lossless compression algorithms are evaluated on a variety of real, current astronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial telescopes. compression techniques herein analyzed are chosen to be representative of the most recent algorithms devised for astronomical data, as well as the most commonly employed compressors employed in real observatories. Experimental results suggest that coding techniques such as RICE and HCOMPRESS, typically employed in world-class observatories such as Roque de los Muchachos, do not produce the best possible lossless compression results. Instead, JPEG-LS, LZMA and NDZIP yield the best compression ratio results for 16-bit data (2.72), floating-point data (2.38) and radio data (1.81), respectively. Therefore, the efficiency with which data are stored and transmitted by these observatories could be significantly improved by selectively employing the aforementioned algorithms.

关键词： Observatories Image coding Data compression Compressors Encoding compression algorithms

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Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第8期39卷 2572-2589页

作者： Cui, Laizhong Su, Xiaoxin Zhou, Yipeng Pan, Yi Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Macquarie Univ Fac Sci & Engn Dept Comp Sydney NSW 2109 Australia Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Sci Fac Comp Sci & Control Engn Shenzhen Inst Adv Technol Shenzhen 518053 Peoples R China

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models (such as CNN models), heavy communication traffic can be incurred by exchanging model updates via the Internet between clients and the parameter server (PS), implying that the network resource can be easily exhausted. Compressing model updates is an effective way to reduce the traffic amount. However, a flexible unbiased compression algorithm applicable for both uplink and downlink compression in FL is still absent from existing works. In this work, we devise the Model Update compression by Soft Clustering (MUCSC) algorithm to compress model updates transmitted between clients and the PS. In MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of each model update. Moreover, we prove that: 1) The compressed model updates are unbiased estimation of their original values so that the convergence rate by transmitting compressed model updates is unchanged;2) MUCSC can guarantee that the influence of the compression error on the model accuracy is minimized. Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster. B-MUCSC is suitable for scenarios with very scarce network resource. Ultimately, we conduct extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate that our algorithms can not only substantially reduce the volume of communication traffic in FL, but also improve the training efficiency in practical networks.

关键词： Computational modeling Training Adaptation models Convergence Data models compression algorithms Uplink Federated learning model update compression convergence rate clustering

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Embedded Streaming Principal Components Analysis for Network Load Reduction in Structural Health Monitoring

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IEEE INTERNET OF THINGS JOURNAL 2021年第6期8卷 4433-4447页

作者： Burrello, Alessio Marchioni, Alex Brunelli, Davide Benatti, Simone Mangia, Mauro Benini, Luca Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy Univ Trento Dept Ind Engn I-38123 Trento Italy Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

Principal component analysis (PCA) is a well-established approach commonly used for dimensionality reduction. However, its computational cost and memory requirements hamper the adoption of PCA in heavily resource-constrained embedded platforms. Streaming approaches have been proposed that may enable embedded implementations of the PCA. Among them, the history PCA (HPCA) algorithm stands out for its robustness to the variability in parameters and accuracy. This article presents a parallel and memory-efficient implementation of HPCA in a structural health monitoring (SHM) application based on a heterogeneous network with sensor nodes measuring three-axial accelerations and gateways collecting measurements from several nodes and sending them to the cloud storage and analytic facility. In the targeted application, standard PCA reaches 15x compression factor with an average reconstruction signal-to-noise ratio of 16 dB and a negligible impact on the accuracy in the tracking of structural modal frequencies. By embedding HPCA on our SHM network gateways, we achieve the same compression factor as standard PCA, with more than 1000x reduction in data memory footprint for running the algorithm. Furthermore, we parallelize HPCA on the gateway, and we achieve a speedup of 7.1x (on 8 cores). Finally, we explore a fixed-point HPCA implementation on sensors (network end nodes), that maximally distributes compression workload, minimizes required communication bandwidth, and maintains the same quality of reconstruction as HPCA in floating point, with a compression factor of 10x.

关键词： Principal component analysis Logic gates Internet of Things Monitoring Cloud computing compression algorithms Data compression Edge computing embedded platforms Internet of Things (IoT) streaming principal component analysis (PCA) structural health monitoring (SHM)

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End-to-End Compressed Meshlet Rendering

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COMPUTER GRAPHICS FORUM 2024年第1期43卷 e15002-e15002页

作者： Mlakar, D. Steinberger, M. Schmalstieg, D. Graz Univ Technol Graz Austria Univ Stuttgart Stuttgart Germany

In this paper, we study rendering of end-to-end compressed triangle meshes using modern GPU techniques, in particular, mesh shaders. Our approach allows us to keep unstructured triangle meshes in GPU memory in compressed form and decompress them in shader code just in time for rasterization. Typical previous approaches use a compressed mesh format only for persistent storage and streaming, but must decompress it into GPU memory before submitting it to rendering. In contrast, our approach uses an identical compressed format in both storage and GPU memory. Hence, our compression method effectively reduces the in-memory requirements of huge triangular meshes and avoids any waiting times on streaming geometry induced by the need for a decompression stage on the CPU. End-to-end compression also means that scenes with more geometric detail than previously possible can be made fully resident in GPU memory. Our approach is based on a novel decomposition of meshes into meshlets, i.e. disjoint primitive groups that are compressed individually. Decompression using a mesh shader allows de facto random access on the primitive level, which is important for applications such as selective streaming and fine-grained visibility computation. We compare our approach to multiple commonly used compressed meshlet formats in terms of required memory and rendering times. The results imply that our approach reduces the required CPU-GPU memory bandwidth, a frequent bottleneck in out-of-core rendering. Our approach allows us to keep unstructured triangle meshes in GPU memory in compressed form and decompress them in the mesh shader just in time for ***

关键词： modeling compression algorithms

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compression of Text in Selected Languages-Efficiency, Volume, and Time Comparison

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SENSORS 2022年第17期22卷 6393-6393页

作者： Stecula, Beniamin Stecula, Kinga Kapczynski, Adrian Silesian Tech Univ Fac Appl Math Akad 2A PL-44100 Gliwice Poland Silesian Tech Univ Fac Org & Management Akad 2A PL-44100 Gliwice Poland

The goal of the research was to study the possibility of using the planned language Esperanto for text compression, and to compare the results of the text compression in Esperanto with the compression in natural languages, represented by Polish and English. The authors performed text compression in the created program in Python using four compression algorithms: zlib, lzma, bz2, and zl4 in four versions of the text: in Polish, English, Esperanto, and Esperanto in x notation (without characters outside ASCII encoding). After creating the compression program, and compressing the proper texts, authors conducted an analysis on the comparison of compression time and the volume of the text before and after compression. The results of the study confirmed the hypothesis, based on which the planned language, Esperanto, gives better text compression results than the natural languages represented by Polish and English. The confirmation by scientific methods that Esperanto is more optimal for text compression is the scientific added value of the paper.

关键词： processing compression coding compression algorithms languages

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