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

共 16713 条记录，以下是201-210 订阅

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Multi-User Probabilistic Semantic Communication with Semantic compression Ratio Optimization 59

Multi-User Probabilistic Semantic Communication with Semanti...

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

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe You, Changsheng Yang, Qianqian Xu, Wei Zhang, Zhaoyang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Prov Key Lab Info Proc Commun Netw IPCAN Hangzhou Peoples R China Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ Miami Inst Data Sci & Comp Coral Gables FL 33146 USA Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China

ISBN: (纸本)9798350304060;9798350304053

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized raw data before transmitting them to a multi-antenna base station (BS). Our model represents the raw data through comprehensive knowledge graphs, utilizing shared probability graphs between users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of equivalent rate of all users, considering total power budget constraint. This joint optimization problem inherently addresses the delicate balance between transmission efficiency and computational complexity. To address this optimization challenge, we present an iterative algorithm in which the optimal solution for the semantic compression ratio of a specific user is determined at each iteration. Numerical results validate the effectiveness of the proposed scheme.

关键词： Semantic communication probability graph resource allocation

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A Highly Scalable Parallel Design for data compression

A Highly Scalable Parallel Design for Data Compression

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2024 ieee High Performance Extreme Computing conference, HPEC 2024

作者： Raut, S. Biplab Amd India Private Limited Bangalore India

ISBN: (纸本)9798350387131

With ever-increasing use of digital data, many applications rely on data compression for their needs related to processing, storage and communication over the network of large volumes of data. While compression saves the memory/disk space and decreases the communication time, there is a considerable runtime spent in this process. Parallel compression algorithms and solutions that are developed to speed up the operations do not scale well on the multi-core CPUs. The data parallel schemes implemented by the prior arts are inefficient in partitioning the data and scaling the performance on the multi-core processors. Another major drawback of the existing multi-threaded compression solutions is the non-compliance to the single-threaded compression format. In this paper, we propose a set of novel and high-performance parallel compression and decompression schemes. We introduce novel designs for dynamic threading based parallel compression and random access point based parallel decompression. With our solution, we mitigate both the scaling issues on multi-core x86 CPUs and format compliance issues encountered in multi-threading the compression operations. Our test results demonstrate massive speedups by manyfolds and performance scaling never seen before on x86 CPUs especially AMD's 'ZZn'-based recent processors that come with very high core counts. © 2024 ieee.

关键词： Random access storage

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compression of Ultra High Definition Image based on K-means Clustering Algorithm 4

Compression of Ultra High Definition Image based on K-means ...

引用

4th ieee International conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Wang, Pengbo Shanghai Zhongqiao Vocational and Technical University Shanghai201514 China

ISBN: (纸本)9798350363586

Image compression plays a key role in contemporary digital environments given the escalating influx of visual data on the Internet. In this study, the K-means clustering algorithm is adopted for Ultra High Definition (UHD) image compression. The procedures are introduced in detail, and a numerical example is provided to demonstrate the applicability of the proposed approach. The results indicate that with an appropriately assigned K value, that is, the number of clusters, the K-means can strike a good balance between the visual quality loss and file size reduction, achieving a compression ratio of 60.37. This study not only proposes an approach for UHD image compression, but also exhibits the promising potential of clustering algorithms in the field of image processing. © 2024 ieee.

关键词： Image compression

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An Adaptive Intra-frame Quantization Parameter Derivation Model Jointing with Inter-frame Analysis

An Adaptive Intra-frame Quantization Parameter Derivation Mo...

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data compression conference (DCC)

作者： Ren, Huiwen Wang, Shanshe Ma, Siwei Gao, Wen Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798350347951

This paper proposes a novel quantization parameter (QP) derivation module that constructs several spatiotemporal characteristics into key-frame QP determination through an efficient pre-analysis progress. A series of simplified prediction modes and a histogram statistic are employed to model the reference quality that key-frames provide to subsequent frames. An adaptive delta-QP value is generated to address the conflict between the low compression efficiency of intra-only frames and the critical predictive basis of temporal-underlying frames. The experimental result shows that the proposed method reduces 41.91% of peak-to-valley bitrate difference while leading to a 0.02% BDBR performance change, indicates that high-quality key-frames may not be indispensable in nowadays video compression frameworks. The proposed method has shown that the pre-analysis based QP optimization for intra-only frames is promising for the enhancement of transmission bandwidth utilization, which may hopefully provide new inspiration for bit allocation and rate control designs.

关键词： Quantization Rate Control Video compression

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Optimizing Bandwidth Utilization Through Word Based compression in Main Memories 38

Optimizing Bandwidth Utilization Through Word Based Compress...

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38th International conference on VLSI Design, VLSID 2025

作者： Aswathy, N.S. Verma, Harsh Kapoor, Hemangee K. IIT Guwahati Department of CSE India

ISBN: (纸本)9798331522445

Memory compression maximizes memory capacity, lowers bandwidth requirements, and reduces energy consumption. State-of-the-art compression techniques mostly focus on enhancing memory capacity, which involves complex reasoning for memory-based data retrieval. In this paper, we propose conditional frequent word compression (Cond-FWC) to reduce bandwidth requirements and energy consumption. The proposed Cond-FWC, as its name implies, eliminates the words that appear the most frequently from the cache line to compress the block. Furthermore, the method accurately places compressed data and metadata for compression with the help of an existing rank subsetting policy. Cond-FWC yields a 16.32% performance improvement and 55% energy reduction compared to an uncompressed non-sub-ranked baseline through rank subsetting and frequent word compression. © 2025 ieee.

关键词： Bandwidth compression

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SmrtComp: Intelligent and Online CAN data compression 26

SmrtComp: Intelligent and Online CAN Data Compression

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ieee 26th International conference on Intelligent Transportation Systems (ITSC)

作者： Tanwar, Dipika Singla, Priyanka Nag, Soham Kumar, Vireshwar Sarangi, Smruti R. IIT Delhi Dept Comp Sci & Engn New Delhi India IIT Delhi Sch Informat Technol New Delhi India IIT Delhi Ctr Excellence Cyber Syst & Informat Assurance New Delhi India

ISBN: (纸本)9798350399462

In modern vehicles, efficiently storing CAN bus data is crucial for on-board diagnostics, performance monitoring, analysis, and the investigation of failures and accidents. In past work, an EDR (Event data Recorder) akin to the ones in aircrafts has been mooted. The state-of-the-art in this field comprises proposals that propose efficient lossless compression of CAN data for such analyses - this limits the data storage capacity. Our contribution SmrtComp aims to achieve a much larger storage efficiency by storing recent data in a lossless format and compressing older data in increasingly lossy formats. The system tries to approximately adhere to an expected accuracy vs time curve (a QoS metric) that is specified a priori. Our study evaluates SmrtComp's performance using various metrics such as the compression gain (CG), root mean square error (RMSE), total data storage and the preservation of key features. SmrtComp was implemented on an ARM Beaglebone board;it was used to store realistic traces and synthetic traces obtained from CAN bus simulators. We achieve a 3.2x higher data storage efficiency as compared to the closest competing work and outperform a popular lossy algorithm by 94.33% in terms of the RMSE. SmrtComp achieves line-speed compression of CAN bus data making it a promising solution for managing large data volumes, and we also show that our compression method preserves anomalies. To the best of our knowledge, this is the first hybrid and tunable compression system in this domain.

关键词： data compression CAN bus Embedded system Event data recorder Memory efficiency

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Optimizing Scientific data Transfer on Globus with Error-bounded Lossy compression 43

Optimizing Scientific Data Transfer on Globus with Error-bou...

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43rd ieee International conference on Distributed Computing Systems (ICDCS)

作者： Liu, Yuanjian Di, Sheng Chard, Kyle Foster, Ian Cappello, Franck Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9798350339864

The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area bandwidth often leads to bottlenecks in research progress. However, in many cases, consuming applications (e.g., for analysis, visualization, and machine learning) can achieve acceptable performance on reduced-precision data, and thus researchers may wish to compromise on data precision to reduce transfer and storage costs. Error-bounded lossy compression presents a promising approach as it can significantly reduce data volumes while preserving data integrity based on user-specified error bounds. In this paper, we propose a novel data transfer framework called Ocelot that integrates error-bounded lossy compression into the Globus data transfer infrastructure. We note four key contributions: (1) Ocelot is the first integration of lossy compression in Globus to significantly improve scientific data transfer performance over wide area network (WAN). (2) We propose an effective machine-learning based lossy compression quality estimation model that can predict the quality of error-bounded lossy compressors, which is fundamental to ensure that transferred data are acceptable to users. (3) We develop optimized strategies to reduce the compression time overhead, counter the compute-node waiting time, and improve transfer speed for compressed files. (4) We perform evaluations using many real-world scientific applications across different domains and distributed Globus endpoints. Our experiments show that Ocelot can improve dataset transfer performance substantially, and the quality of lossy compression (time, ratio and data distortion) can be predicted accurately for the purpose of quality assurance.

关键词： Lossy compression Performance data Transfer Globus WAN

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EFFICIENT LEARNED WAVELET IMAGE AND VIDEO CODING 31

EFFICIENT LEARNED WAVELET IMAGE AND VIDEO CODING

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2024 International conference on Image Processing

作者： Meyer, Anna Prativadibhayankaram, Srivatsa Kaup, Andre Friedrich Alexander Univ Erlangen Nunrnberg Multimedia Commun & Signal Proc Erlangen Germany Fraunhofer Inst Integrated Circuits Moving Picture Technol Erlangen Germany

ISBN: (纸本)9798350349405;9798350349399

Learned wavelet image and video coding approaches provide an explainable framework with a latent space corresponding to a wavelet decomposition. The wavelet image coder iWave++ achieves state-of-the-art performance and has been employed for various compression tasks, including lossy as well as lossless image, video, and medical data compression. However, the approaches suffer from slow decoding speed due to the autoregressive context model used in iWave++. In this paper, we show how a parallelized context model can be integrated into the iWave++ framework. Our experimental results demonstrate a speedup factor of over 350 and 240 for image and video compression, respectively. At the same time, the rate-distortion performance in terms of Bjontegaard delta bitrate is slightly worse by 1.5% for image coding and 1% for video coding. In addition, we analyze the learned wavelet decomposition by visualizing its subband impulse responses.

关键词： Learned image compression learned video compression discrete wavelet transform interpretability

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A JOINT data compression AND TIME-DELAY ESTIMATION METHOD FOR DISTRIBUTED SYSTEMS VIA EXTREMUM ENCODING 49

A JOINT DATA COMPRESSION AND TIME-DELAY ESTIMATION METHOD FO...

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49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weiss, Amir Kochman, Yuval Wornell, Gregory W. Bar Ilan Univ Fac Engn Ramat Gan Israel Hebrew Univ Jerusalem Sch Comp Sci & Engn Jerusalem Israel MIT Res Lab Elect 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350344868;9798350344851

Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two non co-located sensors. In this setting, when joint processing of the received signals is not possible, a compression technique that is tailored to TDE is desirable. For our basic TDE formulation, we develop such a joint compression-estimation strategy based on the notion of what we term "extremum encoding", whereby we send the index of the maximum of a finite-length time-series from one sensor to another. Subsequent joint processing of the encoded message with locally observed data gives rise to our proposed time-delay "maximum-index"-based estimator. We derive an exponentially tight upper bound on its error probability, establishing its consistency with respect to the number of transmitted bits. We further validate our analysis via simulations, and comment on potential extensions and generalizations of the basic methodology.

关键词： Time-delay estimation data compression distributed estimation compression for estimation max-index estimator

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REDEFINING VISUAL QUALITY: THE IMPACT OF LOSS FUNCTIONS ON INR-BASED IMAGE compression 31

REDEFINING VISUAL QUALITY: THE IMPACT OF LOSS FUNCTIONS ON I...

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2024 International conference on Image Processing

作者： Catania, Lorenzo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy

ISBN: (纸本)9798350349405;9798350349399

Implicit Neural Representations (INR) are a novel data representation technique which is gaining ground in the image compression field due to its simplicity and interesting results in terms of rate/distortion ratio. Although a variety of methods based on this paradigm were proposed, limited interest has been given to the analysis of the loss function and the impact of compression artifacts on the visual quality of the reconstructed images, which are mainly due to the adoption of the simple Mean Squared Error (MSE) loss function and to the evaluation done merely in terms of Peak Signal-to-Noise Ratio (PSNR), which do not often correlate with the human perception. In this paper, we evaluate a set of five loss functions in the context of training INRs for image compression, applied to three state-of-the-art architectures, and evaluate their effect on a broader collection of quantitative metrics and the visual fidelity of the decoded images to the originals. The presented outcomes show that the reconstructions obtained by training with some loss functions as MSE suffer from over-smoothing and aliasing artifacts. Our findings reveal that through the employing of a suitable loss function, state-of-the-art architectures quantitatively and qualitatively outperform the results reported in their original papers.

关键词： Image compression Implicit neural representations

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