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检索条件"任意字段=IEEE Data Compression Conference"

共 16774 条记录，以下是661-670 订阅

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Efficient Text compression Algorithms: Principles, Performance, and Applications 5

Efficient Text Compression Algorithms: Principles, Performan...

引用

5th International conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sriman, B. Stephi, Adlin Annie Silviya, S.H. Sangeetharani, M. Shreya, B. Sivakumar, S. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sree Sastha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

Textual data is a fundamental element of human communication and information exchange, playing a pivotal role in a wide array of applications across various domains. However, the digital age has ushered in an era of unprecedented data growth, resulting in the need for efficient storage, transmission, and processing of vast amount of text. Lossless text compression techniques emerge as a vital solution to this challenge, offering the ability to compactly represent textual content without compromising any information. This research study explores the lossless text compression algorithms by investigating the underlying principles, performance, and applications. © 2024 ieee.

关键词： Digital storage

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data compression Approach based on BeiDou Short Message Communication in Power Systems 3

Data Compression Approach based on BeiDou Short Message Comm...

引用

3rd ieee International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Luo, Ruixue Wang, Xin Ma, Chong Song, Guanglei Li, Yu The Information and Communication Company of State Grid Xinjiang Electric Power Co. Ltd Xinjiang China

ISBN: (纸本)9798350317022

Short Message Communication (SMC) is a featured service of BeiDou Navigation Satellite System (BDS). The short-message processing resources for satellites are comparatively unusual. To enhance a resource utilization as well as guarantee short-message fatal quality is suitable, it is essential to allocate as well as plan short-message satellite dispensation properties in multi-satellite coverage area. In this research, the Dynamic Weight based Grey Wolf Optimization (DWGWO) is proposed for the for lossy data compression approach to transmit a electric data based on BDS. The performance of the proposed method achieves better results and it evaluated by using the various metrices such as compression Ratio (CR), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and computational time of values about 66.34, 3.129, 3.998 and 0.004 respectively. The proposed method achieves better results when compared to the existing methods such as Chaos Multilayer Particle Swarm Optimization (CMPSO), Quadrature Mirror Filter (QMF)-based sub-band splitting as well as Binary Encoding Method (MBE). © 2023 ieee.

关键词： BeiDou Navigation Satellite System data compression Dynamic Weight based Grey Wolf Optimization Lossy data and Short Message Communication

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Enhancing Image Quality with Smoothing, Filtering, Fusion, and compression Techniques 7

Enhancing Image Quality with Smoothing, Filtering, Fusion, a...

引用

7th International conference on Contemporary Computing and Informatics, IC3I 2024

作者： Chandrashekar, Rakesh Kumar, Kirana Lakhanpal, Sorabh Smitha, K. Reddy, R. Akhilesh Albawi, Ali New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Telangana Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201312 India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350350067

Adaptive smoothing, selective filtering, multiscale fusion, and compression are all good ways to improve picture quality and get around this problem. Adaptive smoothing changes how much the picture is smoothed based on its local properties. This keeps important features and reduces noise. Sobel and bidirectional filters are examples of selective filtering. It improves features while keeping lines. The MultiScale Fusion and compression (MSFC) method stores and mixes data better by combining wavelet transform-based fusion with JPEG compression. The suggested method works better than tried-and-true methods like wavelet fusion, JPEG compression, and HEVC. Results from the study show that this method is a good mix of compression, selected feature enhancement, and freedom, which makes it a good choice for many image-improving tasks. © 2024 ieee.

关键词： Wiener filtering

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MPEG-G Reference-Based compression of Unaligned Reads Through Ultra-Fast Alignments

MPEG-G Reference-Based Compression of Unaligned Reads Throug...

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data compression conference (DCC)

作者： Ozturk, U. Casale-Brunet, S. Ribeca, P. Mattavelli, M. Ecole Polytech Fed Lausanne EPFL SCI STI MM Lausanne Switzerland Biomathemat & Stat Scotland Edinburgh Scotland

ISBN: (纸本)9781665478939

With the widespread application of next generation sequencing technologies, the volume of sequencing data became comparable to that of big data domains. The compression of sequencing reads (nucleotide sequences, quality values, read names), in both raw and aligned data, is a way to alleviate bandwidth, transfer, and storage requirements of genomics pipelines. ISO/IEC MPEG-G standardizes the compressed representation (i.e. storage and streaming) of structured, indexed sets of genomic sequencing data for both raw and aligned data. For the latter, reference-based compression is a strategy used to compress nucleotide sequences of sequencing reads by using alignment information to a reference sequence, which can be used to represent nucleotide sequences by storing the starting position of the alignment on the reference sequence, and the differences between the reference and the actual read. This general scheme is implemented in different ways by genomic data compressors, such as DeeZ, Quip, and CRAM, which apply to aligned reads.

关键词： Sequential analysis ISO Standards Pipelines Genomics Transform coding Big data Compressors

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Efficient Neural data compression for Machine Type Communications via Knowledge Distillation

Efficient Neural Data Compression for Machine Type Communica...

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ieee Global Communications conference (GLOBECOM)

作者： Hussien, Mostafa Xu, Yi Tian Wu, Di Liu, Xue Dudek, Gregory Samsung AI Ctr Montreal Montreal PQ Canada

ISBN: (纸本)9781665435406

The anticipated huge number of devices and large traffic volumes impose new challenges on the communication system requirements and design. One of the main requirements of massive machine-type communication (mMTC) is to support network energy efficiency. data compression is a widely adopted technique that enables higher energy efficiency, lower latency, and better bandwidth utilization. Unfortunately, the current compression techniques are mainly designed for human-type communications (HTC). Therefore, they consider the reconstruction fidelity, rather than the accuracy of inferred decisions, as the sole performance metric. In this work, we propose a novel encoder for data compression in mMTC communications, which is termed Distillation Encoder (DE). Unlike prior work, the design of the proposed DE aims to achieve high compression ratios while preserving the accuracy of the inferred decisions. DE inherits the knowledge of a large teacher model (trained on the raw data) through knowledge distillation. Evaluating the proposed framework on several public datasets shows a clear performance advantage compared with baseline models in terms of the inferred decision accuracy and generalizing to yet-unseen data. Moreover, the DE can be applied to learn efficient quantizers, as shown in the results.

关键词： Knowledge distillation massive machine-type communications autoencoders lossy compression

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Adaptive Compressed Sensing for Real-Time Video compression, Transmission, and Reconstruction 10

Adaptive Compressed Sensing for Real-Time Video Compression,...

引用

10th ieee International conference on data Science and Advanced Analytics, DSAA 2023

作者： Zhao, Yaping Zeng, Qunsong Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong Access - Ai Chip Center for Emerging Smart Systems Hong Kong

ISBN: (纸本)9798350345032

The real-time transmission of videos with both high resolution and high frame rate is challenging, due to the limited storage space and significant communication overhead. To meet the real-time requirement, these issues are usually tackled by video quality reduction, which compromises the user experience. While previous methods, such as video compressed sensing, have attempted to address these issues, they often employ a fixed compression rate without considering the varying channel gain and do not adequately address the real-time transmission requirements. To mitigate these shortcomings, we propose an adaptive compressed sensing framework that optimizes the compression rate based on the channel state. This approach equivalently optimizes the video quality while ensuring real-time transmission by reducing communication overhead and thus latency. The feasibility and performance of our method are validated and discussed through extensive experiments on both classic and custom datasets. © 2023 ieee.

关键词： Image compression

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Spatiotemporally Adaptive compression for Scientific dataset with Feature Preservation - A Case Study on Simulation data with Extreme Climate Events Analysis 19

Spatiotemporally Adaptive Compression for Scientific Dataset...

引用

19th ieee International conference on e-Science, e-Science 2023

作者： Gong, Qian Zhang, Chengzhu Liang, Xin Reshniak, Viktor Chen, Jieyang Rangarajan, Anand Ranka, Sanjay Vidal, Nicolas Wan, Lipeng Ullrich, Paul Podhorszki, Norbert Jacob, Robert Klasky, Scott Oak Ridge National Laboratory Oak RidgeTN United States Lawrence Livermore National Laboratory LivermoreCA United States University of Kentucky LexingtonKY United States University of Alabama BirminghamAL United States University of Florida GainesvilleFL United States Georgia State University AtlantaGA United States Argonne National Laboratory LemontIL United States

ISBN: (纸本)9798350322231

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5-11% computational overhead. © 2023 ieee.

关键词： Errors

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Learning Tucker compression for Deep CNN

Learning Tucker Compression for Deep CNN

引用

data compression conference (DCC)

作者： Hao, Pengyi Li, Xiaojuan Wu, Fuli Zhejiang Univ Technol Hangzhou Peoples R China

ISBN: (纸本)9781665478939

Recently, tensor decomposition approaches are used to compress deep convolutional neural networks (CNN) for getting a faster CNN with fewer parameters. However, there are two problems of tensor decomposition based CNN compression approaches, one is that they usually decompose CNN layer by layer, ignoring the correlation between layers, the other is that training and compressing a CNN is separated, easily leading to local optimum of ranks. In this paper, Learning Tucker compression (LTC) is proposed. It gets the best tucker ranks by jointly optimizing of CNN's loss function and Tucker's cost function, which means that training and compressing is carried out at the same time. It can directly optimize the CNN without decomposing the whole network layer by layer and can directly fine-tune the whole network without using fixed parameters. LTC is verified on two public datasets. Experiments show that LTC can make a network like ResNet, VGG faster with nearly the same classification accuracy, which surpasses current tensor decomposition approaches.

关键词： Training Tensors Correlation data compression Cost function Convolutional neural networks

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A Fast compression Method of Photoplethysmogram Signal

A Fast Compression Method of Photoplethysmogram Signal

引用

2023 ieee International conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Yang, Liming Zhou, Yan Yao, An Yao, Chongyang Chou, Yongxin School of Cyberspace Security Changzhou College of Information Technology Changzhou China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China School of Mechanical Engineering Yancheng Institute of Technology Yancheng China School of Electrical and Automatic Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9798350309614

The aim of this study is to store more raw photoplethysmogram(PPG) signals for analysis in limited storage space, due to the problem of mass data gathering when the wearable device detects dynamic PPG signals for a long time. Hence this study proposes a method that includes PPG data sampling and compression, PPG data decompression and analysis. Firstly, data is sampled in a sliding window and noise in the signal is suppressed by an improved adaptive filtering method;Then, the preprocessed data in the current data buffer is compressed, and the compressed data is encoded and sent;next, the compressed data is received and stored, and the decoded and recovered data is analysed. Using the simulated PPG signal and the measured PPG signal as the experimental data, the results show that compared with the existing method, the proposed method can effectively improve the compression ratio within the acceptable error range between the restored data and the original data, and the compressed data can be used for heart rate extraction from pulse signals. The proposed method is expected to be used in smart wearable devices with high real-time requirements such as wristbands and watches. © 2023 ieee.

关键词： improved adaptive filtering PPG signal compression real-time PPG signal processing sliding window iteration

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Jointly Training of Binary 3D CNN Features for Action Recognition

Jointly Training of Binary 3D CNN Features for Action Recogn...

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data compression conference (DCC)

作者： Cai, Yangang Xing, Peiyin Wang, Zhenyu Wang, Ronggang Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665478939

This paper presents a novel method to train the quantized feature with the action recognition task jointly. A quantization and inverse-quantization layers are introduced to the 3D CNN. The quantization and the action recognition loss functions are minimized jointly. That is, the method aims to learn the feature not only to improve action recognition accuracy but also reduce the information loss of the quantization. The framework is shown in Fig. (1).

关键词： Training Quantization (signal) Three-dimensional displays data compression Task analysis

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