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

共 16713 条记录，以下是211-220 订阅

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Graph Neural Networks for Precision-Guaranteed compression of Large-scale data

Graph Neural Networks for Precision-Guaranteed Compression o...

引用

2024 ieee International conference on Big data, Bigdata 2024

作者： Khayambashi, Kamiar Alemazkoor, Negin University of Virginia Department of Civil and Environmental Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350362480

Recent advancements in information technology and the development of smart infrastructure within smart cities have led to an unprecedented increase in data generation. Such large volumes of data can easily overburden data storage and management systems, potentially impeding the full exploitation of the data. Existing traditional and prediction-based compression methods have been applied to reduce data storage needs. However, these methods often struggle with issues such as limited compression ratios and no direct control over decompression error, particularly when tested on large-scale datasets. To address these challenges, this study, for the first time, explores the application of advanced spatio-temporal models such as Graph Neural Networks (GNNs). Specifically, we propose a novel prediction-based compression method using GNN that guarantees the desired decompression precision. We demonstrate the efficacy of the proposed method on three large-scale datasets, including traffic data, smart meter energy consumption data, and renewable power generation data, each incorporating data from thousands of locations. The results demonstrate the scalability and robustness of the proposed method across different data types. Moreover, it outperforms state-of-the-art methods in the literature in terms of accuracy and compression efficiency. © 2024 ieee.

关键词： data accuracy

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ADAPTIVE DOWNSAMPLING AND SPATIAL UPCONVERSION FOR POINT CLOUD compression 31

ADAPTIVE DOWNSAMPLING AND SPATIAL UPCONVERSION FOR POINT CLO...

引用

2024 International conference on Image Processing

作者： Zhou, Yichen Zhang, Xinfeng Xu, Yingzhan Zhang, Kai Zhang, Li Univ Chinese Acad Sci Beijing Peoples R China Bytedance Inc Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Although ultra-high resolution point clouds have been acquired more easily, the huge amount of the data makes it more challenging to store and transmit, and also difficult to be applied in lightweight terminal. To address this challenge, we propose an Adaptive Downsampling and Spatial Upconversion framework for point cloud compression (ADSU). In the proposed adaptive downsampling, we introduce two key components, feature-aware augmented graph convolution (FAGC) and adaptive-sampling-based global graph aggregation module (ASGGA), to capture correlations between local and global features. For upsampling, we propose a high-frequency feature generation module (HFFG) to generate detailed information, which plays a crucial role in achieving precise reconstruction. Experimental results demonstrate that the combination of our proposed ADSU with popular point cloud compression methods can significantly improve compression performance.

关键词： compression graph convolution point cloud downsampling point cloud upsampling

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SoK: compression in Rollups 6

SoK: Compression in Rollups

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6th ieee International conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Palakkal, Roshan Gorzny, Jan Derka, Martin Quantstamp Inc United States Zircuit Canada

ISBN: (纸本)9798350316742

A rollup is a scaling solution built on top of an existing blockchain. Rollups separate execution from consensus, but are required to post the data used for state updates to the underlying blockchain. This data is required to ensure that execution of state updates are performed correctly. As writing data to a public blockchain is not free, rollups are incentivized to minimize the amount of data they post on-chain. Rollups therefore aggregate and compress the data used for executions in order to save on fees associated with writing data to the blockchain. In this work, we explore the methods for posting data on-chain and the compression techniques used by real-world rollups. We explore differences in implementations and contrast the approaches used by both optimistic and zero-knowledge rollups. We also explore approaches which enable domain-specific compression, consider upcoming changes to data storage on Ethereum, and suggest improvements for rollup compression. © 2024 ieee.

关键词： data compression

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Rate-Distortion Optimization for Cross Modal compression

Rate-Distortion Optimization for Cross Modal Compression

引用

data compression conference (DCC)

作者： Gao, Junlong Jia, Chuanmin Wang, Shanshe Ma, Siwei Gao, Wen Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350347951

Recently, cross modal compression (CMC) is proposed to compress highly redundant visual data into a compact, common, human-comprehensible domain (such as text) to preserve semantic fidelity for semantic-related applications. However, CMC only achieves a certain level of semantic fidelity at a constant rate, and the model aims to optimize the probability of the ground truth text but not directly semantic fidelity. To tackle the problems, we propose a novel scheme named rate-distortion optimized CMC (RDO-CMC). Specifically, we model the text generation process as a Markov decision process and propose rate-distortion reward which is used in reinforcement learning to optimize text generation. In rate-distortion reward, the distortion measures both the semantic fidelity and naturalness of the encoded text. The rate for the text is estimated by the sum of the amount of information of all the tokens in the text since the amount of information of each token is a lower bound of coding bits. Experimentally, RDO-CMC effectively controls the rate in the CMC framework and achieves competitive performance on MSCOCO dataset.

关键词： Cross modal compression Rate distortion optimization

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Communication Efficient Federated Learning Based on Combination of Structural Sparsification and Hybrid Quantization Sensing 22

Communication Efficient Federated Learning Based on Combinat...

引用

ieee 22nd International conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wu, Tingting Yu, Wei Guo, Manxue Nie, Wenjing Jia, Zimeng Xin, Lun China Mobile Res Inst Beijing 100053 Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Federated learning obtains the global model through the cooperative training of each participating device while protecting data privacy. However, the huge communication cost caused by each client sending complete model update becomes an important problem for its wide application. Model sparsification and quantization are practical solutions to reduce singleround uplink communication, respectively. However, when the compression ratio increases, (1) the current model sparsification methods based on fine granularity by uploading significant gradients are difficult to effectively select important parameters by using thresholds;(2) the unified bit-width quantization of parameters of layers with different importance in the model will lead to greater information loss. In this paper, we propose a joint compression framework based on functional structure sparsity and hybrid quantization sensing to compress the communication cost to more than two orders of magnitude. First, we proposed to take filters as sparse granularity through the visual analysis of parameter updating rules in the training process, and searched for filters with strong representational ability among filters with different representational abilities in the convolution layer. Second, we propose an adaptive layer-sensing quantization method according to the parameter distribution of each layer, which assigns different bit widths to different layers in the model after structural sparsification. Finally, we design a model aggregation and update scheme based on the above joint compression framework, which reduces the error caused by compression through model reconstruction and parameter reuse. Experiments show that our proposed framework compress single communication to more than two orders of magnitude in different tasks while ensuring the convergence speed and final model performance.

关键词： Federated learning efficient communication model compression quantization edge intelligence

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Design of JPEG2000 Image compression System Based on ADV212 for Satellites

Design of JPEG2000 Image Compression System Based on ADV212 ...

引用

2024 ieee International conference on Control Science and Systems Engineering, ICCSSE 2024

作者： Li, Enshi Zhou, Li Zhu, Yan National Space Science Center of Chinese Academy of Sciences Bejing China

ISBN: (纸本)9798331517199

Due to the limitations of spacecraft data processing capabilities and the data transmission rate to the ground, the image compression system is essential for imaging payloads on satellites. JPEG2000, with its advantages of high compression ratio, high signal-to-noise ratio, support for progressive transmission, and absence of mosaic distortion, is a preferred algorithm for image compression in spaceborne cameras. In response to the complexity of the JPEG2000 algorithm and the challenges of hardware implementation, this paper presents an image compression system based on the ADV212 image compression chip for realizing JPEG2000 compression. The FPGA is utilized for image buffering and control of the compression system, while the ADV212 chip performs the compression functions. The experimental results validate the high performance and compatibility of the system. This system boasts advantages such as simple hardware and software implementation, high efficiency, superior performance, short development cycle, and low cost, which makes it suitable for aerospace applications, particularly in commercial space satellites. © 2024 ieee.

关键词： Image compression

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Preserving Diagnostic Quality of Image compression using Block Adaptive Models

Preserving Diagnostic Quality of Image Compression using Blo...

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2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Chadhar, Rammilan Tripathi, Satyendra Jain, Rakhee Madhyanchal Professional University School of Electronics and Communication Engineering Bhopal India Tcs Department of Research and Development Indore India Manit Department of Electronics and Communication Engineering Bhopal India

ISBN: (纸本)9798331529833

Information compression is shown to pack the size of data accumulated in advanced documents. We have proposed a block adaptive model (BAM) compression strategy to pack the size of image while keeping the decompression overhead insignificant. In versatile lossless word reference based image compression, when compression proportion is looked at among word reference based methodology. To inspect the presentation of the Lossless and Lossy compression of images and to get high pressure proportion for both lossless and lossy image compression. The fundamental intention is to give acceptable compression proportion which limits the testing existence intricacy. BAM methods produce better improvement in compression effectiveness without adding any extra usefulness or region overhead. The proposed compression strategies will be looked at with other existing techniques regarding quality. The proposed method is implementing MATLAB 2015 and calculated PSNR, MSE and CR. © 2025 ieee.

关键词： Image compression

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FedSZ: Leveraging Error-Bounded Lossy compression for Federated Learning Communications 44

FedSZ: Leveraging Error-Bounded Lossy Compression for Federa...

引用

44th ieee International conference on Distributed Computing Systems (ICDCS)

作者： Wilkins, Grant Di, Sheng Calhount, Jon C. Li, Zilinghan Kim, Kibaek Underwood, Robert Mortiert, Richard Cappello, Franck Argonne Natl Lab Lemont IL 60439 USA Univ Cambridge Cambridge England Clemson Univ Clemson SC USA Univ Illinois Urbana IL USA

ISBN: (纸本)9798350386066;9798350386059

With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and servers becomes crucial. FL, though decentralized, often faces communication bottlenecks, especially in resource-constrained scenarios. Existing data compression techniques like gradient sparsification, quantization, and pruning offer some solutions, but may compromise model performance or necessitate expensive retraining. In this paper, we introduce FEDSZ, a specialized lossy-compression algorithm designed to minimize the size of client model updates in FL. FEDSZ incorporates a comprehensive compression pipeline featuring data partitioning, lossy and lossless compression of model parameters and metadata, and serialization. We evaluate FEDSZ using a suite of error-bounded lossy compressors, ultimately finding SZ2 to be the most effective across various model architectures and datasets including AlexNet, MobileNetV2, ResNet50, CIFAR-10, Caltech101, and Fashion-MNIST. Our study reveals that a relative error bound 10(-2) achieves an optimal tradeoff, compressing model states between 5.55-12.61x while maintaining inference accuracy within < 0.5% of uncompressed results. Additionally, the runtime overhead of FEDSZ is < 4.7% or between of the wall-clock communication-round time, a worthwhile trade-off for reducing network transfer times by an order of magnitude for networks bandwidths < 350Mbps. Intriguingly, we also find that the error introduced by FEDSZ could potentially serve as a source of differentially private noise, opening up new avenues for privacy-preserving FL.

关键词： Lossy compression federated learning edge computing

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Secure Healthcare data Management with Lossless compression and Hyperchaos Encryption

Secure Healthcare Data Management with Lossless Compression ...

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2024 ieee Calcutta conference, CALCON 2024

作者： Rana, Subhadip Pathak, Arghya Mondal, Hrishikesh Mandal, Mrinal Kanti National Institute of Technology Durgapur Department of Physics West Bengal India Durgapur Government College Department of Physics West Bengal India

ISBN: (纸本)9798331516666

Effective medical image management requires a balance between compression and security without compromising quality. This study presents a two-step strategy: first, using lossless JPEG compression to reduce storage requirements while maintaining diagnostic quality. Second, hyper chaos encryption is applied to secure the compressed images by introducing chaotic dynamics for robust data protection. This approach ensures both storage efficiency and security, safeguarding sensitive patient data during transmission and storage in healthcare systems. © 2024 ieee.

关键词： Electronic health record

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Lossless SIMD compression of LiDAR Range and Attribute Scan Sequences

Lossless SIMD Compression of LiDAR Range and Attribute Scan ...

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ieee International conference on Robotics and Automation (ICRA)

作者： Ford, Jeff Ford, Jordan ComplexIQ Atlanta GA 30517 USA Carnegie Mellon Univ Dept Elect Engn Pittsburgh PA 15217 USA

ISBN: (纸本)9798350323658

As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour (Fig. 1) and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR range and attribute scan sequences including multiple-return range, signal, reflectivity, and ambient infrared. Our algorithm-dubbed "Jiffy"-achieves substantial compression by exploiting spatiotemporal redundancy and sparsity. Speed is accomplished by maximizing use of single-instruction-multiple-data (SIMD) instructions. In autonomous driving, infrastructure monitoring, drone inspection, and handheld mapping benchmarks, the Jiffy algorithm consistently outcompresses competing lossless codecs while operating at speeds in excess of 65M points/sec on a single core. In a typical autonomous vehicle use case, single-threaded Jiffy achieves 6x compression of centimeter-precision range scans at 500+ scans per second. To ensure reproducibility and enable adoption, the software is freely available as an open source library(3).

关键词： Autonomous vehicles

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