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

共 16713 条记录，以下是121-130 订阅

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Unlocking the Power of Numbers: Log compression via Numeric Token Parsing 24

Unlocking the Power of Numbers: Log Compression via Numeric ...

引用

39th ACM/ieee International conference on Automated Software Engineering (ASE)

作者： Yu, Siyu Wu, Yifan Li, Ying He, Pinjia Chinese Univ Hong Kong Shenzhen CUHK Shenzhen Shenzhen Peoples R China Peking Univ Beijing Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Parser-based log compressors have been widely explored in recent years because the explosive growth of log volumes makes the compression performance of general-purpose compressors unsatisfactory. These parser-based compressors preprocess logs by grouping the logs based on the parsing result and then feed the preprocessed files into a general-purpose compressor. However, parser-based compressors have their limitations. First, the goals of parsing and compression are misaligned, so the inherent characteristics of logs were not fully utilized. In addition, the performance of parser-based compressors depends on the sample logs and thus it is very unstable. Moreover, parser-based compressors often incur a long processing time. To address these limitations, we propose Denum, a simple, general log compressor with high compression ratio and speed. The core insight is that a majority of the tokens in logs are numeric tokens (i.e. pure numbers, tokens with only numbers and special characters, and numeric variables) and effective compression of them is critical for log compression. Specifically, Denum contains a Numeric Token Parsing module, which extracts all numeric tokens and applies tailored processing methods (e.g. store the differences of incremental numbers like timestamps), and a String Processing module, which processes the remaining log content without numbers. The processed files of the two modules are then fed as input to a general-purpose compressor and it outputs the final compression results. Denum has been evaluated on 16 log datasets and it achieves an 8.7% - 434.7% higher average compression ratio and 2.6x - 37.7x faster average compression speed (i.e. 26.2 MB/S) compared to the baselines. Moreover, integrating Denum's Numeric Token Parsing module into existing log compressors can provide a 11.8% improvement in their average compression ratio and achieve 37% faster average compression speed.

关键词： data compression Log compression Log Analysis

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Improving compression Efficiency using an Encoder -aware Motion Compensated Temporal Filter

Improving Compression Efficiency using an Encoder -aware Mot...

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data compression conference (DCC)

作者： Vanam, Rahul Sethuraman, Sriram Amazon Prime Video Seattle WA 98121 USA

ISBN: (纸本)9798350347951

Motion Compensated Temporal Filtering (MCTF) is a pre-processing approach employed prior to video encoding, for improving the compression efficiency. Prior MCTF designs (e.g. [1]) use pre-defined frame-level quantization parameters (QPs) for different slice types and temporal layers, and operate with a fixed Group of Pictures (GOP) structure. However, commercial encoders can adapt GOP structure based upon content characteristics, and can also adapt QPs on a block-basis based upon the frequency of the block being referenced and the spatial complexity of the block, causing prior MCTF to perform sub-optimally with commercial encoders. © 2023 ieee.

关键词： Signal encoding

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Blurry Video compression A Trade-off between Visual Enhancement and data compression

Blurry Video Compression A Trade-off between Visual Enhancem...

引用

ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Argaw, Dawit Mureja Kim, Junsik Kweon, In So Korea Adv Inst Sci & Technol Seoul South Korea Harvard Univ Cambridge MA USA

ISBN: (纸本)9798350318920;9798350318937

Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video while preserving its quality. In this regard, previous works have achieved remarkable results on videos acquired under specific settings such as instant (known) exposure time and shutter speed which often result in sharp videos. However, when these methods are evaluated on videos captured under different temporal priors, which lead to degradations like motion blur and low frame rate, they fail to maintain the quality of the contents. In this work, we tackle the VC problem in a general scenario where a given video can be blurry due to predefined camera settings or dynamics in the scene. By exploiting the natural trade-off between visual enhancement and data compression, we formulate VC as a min-max optimization problem and propose an effective framework and training strategy to tackle the problem. Extensive experimental results on several benchmark datasets confirm the effectiveness of our method compared to several state-of-the-art VC approaches.

关键词： Algorithms Computational photography image and video synthesis

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Model compression for data compression: Neural Network Based Lossless Compressor Made Practical

Model Compression for Data Compression: Neural Network Based...

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data compression conference (DCC)

作者： Qin, Liang Sun, Jie Huawei Technol Co Ltd Theory Lab Cent Res Inst Labs 2012 Hong Kong Peoples R China

ISBN: (纸本)9798350347951

In the recent few years, lossless compressors based on deep learning emerged as a new research direction. However, existing DL compressors focus mostly on compression ratio at the expense of compression speed (typically 10-1000 times slower than popular compressors, operating at the KB/s level), rendering them impractical to be deployed in industrial settings. To address the throughput issue of network inference, we propose to utilize model compression in DL-based lossless data compression. Our approach originates from the sparse learning framework to flexibly balance the model complexity and the compression ratio. Through systematic numerical experiments, we found that compressed models with only tens of thousands of parameters can still retain competitive compression ratios comparing with the original large models which are typically 100 times larger. The proposed scheme reveals novel properties concerning deep compression models and data-logarithmic relations between the model size and compressed data size for general mixed data, and a turning point indicating minimum representation size for relatively formatted data.

关键词： lasso method lossless compression model compression

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SPCGC: Scalable Point Cloud Geometry compression for Machine Vision

SPCGC: Scalable Point Cloud Geometry Compression for Machine...

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ieee International conference on Robotics and Automation (ICRA)

作者： Xie, Liang Gao, Wei Zheng, Huiming Li, Ge Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350384581;9798350384574

With the proliferation of sensor devices, the extensive utilization of three-dimensional data in multimedia continues to grow. Point clouds are widely adopted within this domain because they are one of the most intuitive representations of three-dimensional data. However, the substantial volume of point cloud data poses significant challenges for storage and transmission. Moreover, a considerable portion of the data loses its semantic information during transmission. Consequently, how can we ensure both the perceptual quality for the human and the performance of downstream tasks during the transmission? To address this issue, we propose a scalable point cloud geometry compression framework (SPCGC) for machine perception. This framework tackles the fidelity issues associated with point cloud compression and preserves more semantic information, enhancing the performance of machine vision tasks. Our solution consists of a base layer bitstream and an enhancement layer bitstream. The base layer bitstream contains geometry data, while the enhancement layer bitstream utilizes semantic-guided residual data. Additionally, we introduce two modules for extracting and coding residual features. And incorporate classification and segmentation losses from downstream tasks into the Rate-Distortion (RD) optimization. Our approach outperforms existing learning-based lossy point cloud coding methods through empirical validation in downstream tasks without sacrificing point cloud compression performance.

关键词： Machine vision

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Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT data 59

Deep Dict: Deep Learning-based Lossy Time Series Compressor ...

引用

59th Annual ieee International conference on Communications (ieee ICC)

作者： Liu, Jinxin Djukic, Petar Kulhandjian, Michel Kantarci, Burak Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Nokia Bell Labs Toronto ON Canada

ISBN: (纸本)9781728190549

We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range. Deep Dict incorporates two essential components: the Bernoulli transformer autoencoder (BTAE) and a distortion constraint. BTAE extracts Bernoulli representations from time series data, reducing the size of the representations compared to conventional autoencoders. The distortion constraint limits the prediction error of BTAE to the desired range. Moreover, in order to address the limitations of common regression losses such as L1/L2, we introduce a novel loss function called quantized entropy loss (QEL). QEL takes into account the specific characteristics of the problem, enhancing robustness to outliers and alleviating optimization challenges. Our evaluation of Deep Dict across diverse time series datasets from various IoT domains reveals that Deep Dict outperforms state-of-the-art lossy compressors in terms of compression ratio by a significant margin by up to 53.66%.

关键词： Internet of Things Machine Learning Deep Learning IoT data compression Lossy Time Series Compressor

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A study on Automotive data compression technology On-device platform 15

A study on Automotive data compression technology On-device ...

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15th International conference on Ubiquitous and Future Networks (ICUFN)

作者： Ro, Yongcheol Shin, Daekyo Jang, Junhyek Jang, Seonghyun Jang, Soohyun Korea Elect Technol Inst KETI Mobil Platform Res Ctr Seongnam South Korea

ISBN: (纸本)9798350385304;9798350385298

Research on autonomous driving cooperation has been extensively studied. However, sensor reliability and negotiation errors are expected in the cooperation process. Thus, it is necessary to compress and store historical data and sed it to a cloud platform for analysis. However, an efficient compression method is required to transmit historical data from the device. In this research, we study on technologies for analyzing vehicle data and compressing vehicle data on the device.

关键词： compression sensor data on-device deeplearning

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compression-based Feature Reduction for Upper Limb Motor Decoding from Peripheral Neural Signal 20

Compression-based Feature Reduction for Upper Limb Motor Dec...

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20th International conference on Body Sensor Networks

作者： Jeong, Byeongchan Wu, Tong Anh Tuan Nguyen Xu, Jian Cheng, Jonathan Keefer, Edward Yang, Zhi Univ Minnesota Twin Cities Dept Biomed Engn Minneapolis MN 55414 USA IQVIA Adv Analyt Wayne PA USA Fasikl Inc Bloomington MN USA Zhejiang Univ Coll Biotechnol & Bioengn Hangzhou Peoples R China Univ Texas Dallas Dept Plast Surg SouthWestern Med Ctr Dallas TX USA

ISBN: (纸本)9798331530143

Objective: Neural prostheses generate an extensive volume of data during neural recording and decoding, presenting challenges in data processing and storage. A strategy to address this issue is compressing the data before transmission. Methods: This paper shows the possibility of efficiently compressing nerve data while achieving improved performance for an upper-limb neural prosthetic device. The new method, named compression-Based Feature Reduction (CBFR), is divided into two stages: (1) applying a lightweight compression method to the original signal, and (2) reducing the number of features after feature extraction, thereby enhancing the classification accuracy in task-based experiments. Results: In an in vivo experiment involving patients with amputated hands, CBFR improved the classification performance of each finger movement up to 1.6% and 6.9% using Symmlet4 Discrete Wavelet Transform (DWT) and Walsh-Hadamard Transform (WHT), respectively, over the original signal. Both methods achieved up to 3x compression ratio from the original signal. Significane: The proposed data compression technique could help advance the field of neural prosthetics by addressing data transmission challenges and enhancing the performance of upper-limb neural prosthetic devices.

关键词： Brain-machine/computer interface data compression feature extraction implantable neural devices

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AOCL-compression - A high performance optimized lossless data compression library

AOCL-Compression - A high performance optimized lossless dat...

引用

ieee High Performance Extreme Computing Virtual conference (HPEC)

作者： Raut, S. Biplab AMD India Private Ltd Bangalore India

ISBN: (纸本)9798350308600

data compression is the process of encoding (or compressing) information using fewer bits than originally present in the data stream or signal. Depending upon whether this process is invertible or not, data compression can be lossless or lossy. While there exists various popular implementations of different lossless data compression methods, they are unable to completely meet the performance requirements demanded by the ever-increasing data usage of the applications. In this paper, we present a comparative survey of different lossless compression methods and introduce a high performance compression library called AOCL-compression optimized for x86 architecture in general and AMD's "Zen"-based processors in particular. This library supports LZ4, LZ4HC, ZLIB, ZSTD, LZMA, BZIP2, and Snappy based compression methods. This paper discusses the design features of the new library framework and the algorithmic optimizations implemented for the different compression methods. Results highlighting the performance benefits of this new library implementation over the reference implementations of the respective compression methods are also presented.

关键词： AOCL AOCL-compression BWT BZIP2 compression database systems Decompression DEFLATE Dictionary coding Entropy coding FSE Hash chain Hash table Huffman coding Lossless data compression LZ4 LZ4HC LZ77 LZ78 LZMA MTF NoSQL RLE Snappy SQL ZLIB ZSTD

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Application of adaptive douglas-peucker with acceleration algorithm in ship trajectory compression 2

Application of adaptive douglas-peucker with acceleration al...

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2nd ieee conference on Artificial Intelligence (CAI)

作者： Yang, Lichao Liu, Jingxian Wang, Yukuan Wuhan Univ Technol Sch Nav State Key Lab Maritime Technol & Safety Wuhan Hubei Peoples R China Wuhan Univ Technol Sch Nav Hainan Inst Sanya Hainan Peoples R China

ISBN: (纸本)9798350354102;9798350354096

In this study, the Adaptive Douglas-Peucker with Acceleration (ADPA) algorithm is introduced for compressing Automatic Identification System (AIS) ship trajectory data. This novel algorithm, tailored for maritime trajectory analysis, dynamically calculates compression thresholds by considering both acceleration and distance to a baseline, thereby enhancing the compression process. Unlike traditional methods, the ADPA algorithm is particularly effective in handling complex trajectory patterns, including circular and semi-circular paths. Empirical analysis using AIS data from the Ningbo-Zhoushan area and the Yangtze River Estuary demonstrated the ADPA algorithm's capability, achieving a compression rate of over 50%, significantly higher than that of standard Douglas-Peucker algorithms. This advanced approach provides a more efficient and accurate means of processing large-scale maritime trajectory data.

关键词： Water traffic Ship trajectory compression Automatic identification system (AIS) adaptive Douglas-Peucker with an acceleration(ADPA) Sliding window

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