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

共 16774 条记录，以下是591-600 订阅

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Lossy compression of Multi-Channel EEG and PPG Signals Based on Golomb-Rice Coding with Parameter Estimation

Lossy Compression of Multi-Channel EEG and PPG Signals Based...

引用

2024 International conference on Advanced Technologies for Communications, ATC 2024

作者： Wanasekara, Senura Hansaja Dung, Han Huy Nguyen, Ngoc Hung Nguyen, Van-Dinh College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Hanoi Viet Nam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Viet Nam

ISBN: (纸本)9798350353983

The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face significant challenges in terms of power consumption, computational complexity, and data storage, necessitating efficient data compression techniques. This paper proposes a modified Golomb-Rice coding method tailored for lossy compression of multi-channel EEG and PPG signals. By leveraging the statistical properties of filtered data and employing an optimized parameter estimation strategy, the proposed method achieves high compression ratios while minimizing computational overhead and preserving critical signal information. The proposed algorithm shifts data to handle negative values, scales and converts it into an appropriate format for encoding, and uses a novel parameter estimation technique to optimize the Golomb-Rice parameter. Extensive simulations on EEG and PPG datasets demonstrate that this parameter estimation method yields significantly better performance, improving both efficiency and processing speed. Results underscore the importance of preprocessing in data compression and provide a robust framework for future advancements in biomedical signal processing. © 2024 ieee.

关键词： Encoding (symbols)

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BOUNCE: Memory-Efficient SIMD Approach for Lightweight Integer compression 38

BOUNCE: Memory-Efficient SIMD Approach for Lightweight Integ...

引用

38th ieee International conference on data Engineering (ICDE)

作者： Hildebrandt, Juliana Habich, Dirk Lehner, Wolfgang Tech Univ Dresden Database Res Grp Dresden Germany

ISBN: (纸本)9781665481045

Integer compression plays an important role in columnar database systems to reduce the main memory footprint as well as to speedup query processing. To keep the additional computational effort of (de)compression as low as possible, the powerful Single Instruction Multiple data (SIMD) extensions of modern CPUs are heavily applied. While a scalar compression algorithm usually compresses a block of N consecutive integers, the state-of-the-art SIMD implementation scales the block size to k*N with k as the number of elements which could be simultaneously processed in an SIMD register. On the one hand, this scaling SIMD approach improves the performance of (de)compression but can lead to a degradation of the compression ratio compared to the scalar variant on the other hand. Within this paper, we analyze this degradation effect for an heavily applied and well-performing integer compression algorithm called BitPacking (BP) and present a novel SIMD concept to overcome that effect. Our novel SIMD idea called BOUNCE is to concurrently compress k different blocks of size N within SIMD registers achieving the same compression rate as the scalar variant. As we are going to show, our proposed SIMD idea works well for BP and may offer a new generalized approach to optimize further algorithms.

关键词： integer compression SIMD memory-efficiency

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Key Technologies on High-capacity Satellite Imagery compression 11

Key Technologies on High-capacity Satellite Imagery Compress...

引用

2023 ieee 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Fang, Huoneng Han, Pengfei Xiao, Huachao Xi'An Institute of Space Radio Technology Information Processing Technology Research Lab Xi'an China Xi'an China

ISBN: (纸本)9798350314014

With the continuous progress of science and technology, the accuracy of satellite remote sensing, detection and reconnaissance technologies is getting higher and higher, and the amount of data generated by them is also getting larger and larger. The amount of image data generated by high-resolution satellite imaging systems often reaches hundreds of Gb/s, which, coupled with the limited transmission bandwidth and storage space constraints, puts great pressure on data caching and transmission. The upper limit of transmission rate provided by traditional satellite data transmission methods has been difficult to meet the demand of inter-satellite and satellite-ground high-speed communication. To address this, a high-capacity satellite image data compression algorithm is proposed. Firstly, for the problem of redundancy of handover satellite image data, image fusion or stitching technology is used to extract valuable information from different images and synthesize high-quality images to reduce data redundancy. Secondly, to address the problem of repeated transmission of re-entry satellite data, multi-temporal data is analyzed to determine the change process of re-entry satellite image features and segment the change region in the image. In the re-entry process, only the information of the change region needs to be transmitted, avoiding multiple transmissions of duplicate information. Finally, to solve the problem of low compression efficiency of existing on-satellite compression algorithms, a new 'image-information-image' data compression strategy is explored to achieve high compression ratio and high fidelity of satellite image compression. In order to achieve high capacity compression and efficient transmission of satellite images with limited bandwidth and storage space, and to lay the foundation for fast and efficient intelligent services for all-day, all-weather observation of the Earth. © 2023 ieee.

关键词： Image compression

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A real-time JPEG image compression hardware design architecture 18

A real-time JPEG image compression hardware design architect...

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18th International conference on Advanced Computing and Analytics, ACOMPA 2024

作者： Khai, Lam Duc Van Quang, Tran Luan, Pham Hoai University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Nara Institute of Science and Technology Ikoma Nara630-0192 Japan

ISBN: (纸本)9798331542467

In technology 4.0, all data is put on the Cloud for transmission and processing. Images are one of the most important data and are quite large because they are created every day. Therefore, the increased demand for image storage and transmission urges scientists to research image compression methods to reduce the excess image data and it can solve the above problem. This paper presents the Field Programmable Gate Array (FPGA) hardware architecture of the JPEG image compression method (the world's most popular image compression method) using FDCT numerically calculated Floating Point combined with Pipeline. The main goal of the proposed method is to increase the processing speed and compression performance of the image. The tests were performed on Red-Green-Blue (RGB) standard images. Evaluated by execution time, mean error value (MSE), top noise ratio (PSNR), and compression ratio (CR). This complete design is synthesized on the Vitex7-VC709 board with a frequency of 100.7 Mhz, throughput of 250 FPS, and power consumption of 2.401 Watts. © 2024 ieee.

关键词： Image compression

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Your Camera Improves Your Point Cloud compression 48

Your Camera Improves Your Point Cloud Compression

引用

48th ieee International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Lin, Yuhuan Xu, Tongda Zhu, Ziyu Li, Yanghao Wang, Zhe Wang, Yan China Tsinghua University Department of Automation China Tsinghua University Department of Computer Science China

ISBN: (纸本)9781728163277

LiDAR point cloud compression is important for autonomous driving as it consumes a lot of storage and bandwidth. Although the fusion of camera and LiDAR for vision perception has been well studied, it remains unexplored that how we can improve the compression of LiDAR point cloud data using cross-modal information from cameras. In this paper' we propose a multi-modality compression framework for LiDAR point cloud by exploiting the depth information predicted from its paired image. To the best of our knowledge' our model is the first multi-modality compression framework for point cloud. Specifically' we first represent point cloud based on octrees to reduce spatial redundancy. Then' we propose a cross-modal fusion structure to improve the compression of these octrees' with depth distribution extracted from the camera pixels and acts as side information. Compared to previous state-of-the-art (SOTA) method, our approach obtains up to 8.10% compression rate gain for LiDAR point cloud compression. © 2023 ieee.

关键词： multi-modality point cloud compression

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Region-Based Retinal Image compression Using Convolutional Auto-Encoder And Arithmetic Encoding

Region-Based Retinal Image Compression Using Convolutional A...

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2024 International conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Dalila, Chikhaoui Mohammed, Beladgham Mohamed, Ben Aissa Tahri Mohammed University Ltit Laboratory Department of Electrical Engineering Bechar Algeria Abou Bekr Belkaid University Ltit Laboratory Department of Electrical Engineering Tlemcen Algeria

ISBN: (纸本)9798350353754

In medical imaging, image compression is used to ease bandwidth and storage requirements. In this paper, we report a Roi based image compression approach for retinal images based on convolutional auto-encoder (CAE) and arithmetic coding, to achieve a better compression efficiency with high quality reconstruction. There are three main sections in our compression scheme: preprocessing of data, CAE-based lossy compression, and arithmetic coding for lossless compression. For data preprocessing, an automatic segmentation is used to extract the ROI and non-ROI parts. The CAE is used to achieve a high compression on the non-ROI, the encoding network learns to produce an optimal compacted representation that maintains the structural information. Using the compact representation, the decoding network reconstructs the decoded image. The ROI portion of the image is losslessly compressed using arithmetic coding to preserve diagnostically important information. In order to validate the performance of our proposed approach, we compared our results with state-of-the-art compression techniques and recent methods. At high compression ratio as 40, the proposed approach achieved a mean of 39.5 dB at signal-to-noise ratio (PSNR) metric and 9 7. 2 8 % similarity in multi-scale structural similarity (MS-SSIM), proving its efficacy. © 2024 ieee.

关键词： Encoding (symbols)

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INTEGER NETWORK FOR CROSS PLATFORM GRAPH data LOSSLESS compression

INTEGER NETWORK FOR CROSS PLATFORM GRAPH DATA LOSSLESS COMPR...

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ieee International conference on Multimedia and Expo (ieee ICME)

作者： Zhang, Ge He, Huanyu Wang, Haiyang Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China Clobotics Bellevue WA USA

ISBN: (数字)9781665472180

ISBN: (纸本)9781665472180

It has been witnessed that the learned data compression techniques has outperformed conventional ones. However, the non-deterministic floating-point calculation makes the probability prediction inconsistent between sender and receiver, disabling practical applications. We propose to use the integer network to relieve this problem and focus on graph data lossless compression. Firstly, we propose an adaptive fixedpoint format, AdaFixedPoint, which can convert a floatingpoint model, which has graph convolution layers to a fixedpoint one with minimal precision loss and enable deterministic graph data lossless compression. Secondly, we propose QbiasFree Compensation and Bin Regularization to quantize the network with fewer bits, relieving the computation cost. Experiments show that our proposed integer network can achieve successful cross-platform graph data compression. And compared with the commonly used 8 bits, our method remarkably decreases the quantized average bit to 5 bits, without a performance drop.

关键词： graph data lossless compression cross platform integer network quantization

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A New JPEG compression Algorithm for Transmission, Processing and Storage of Health data 6

A New JPEG Compression Algorithm for Transmission, Processin...

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6th International conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Avdin, Yilmaz Ozkaynak, Fatih Firat University Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798350344349

This study proposes a new compression algorithm for health data management, which is a derivative of the JPEG algorithm. The proposed method has shown improvement in compression ratio compared to the classical JPEG algorithm, making it a promising solution for the storage and transmission of medical images in PACS systems. The algorithm has been designed with data security in mind and it is planned to integrate it with encryption algorithms in the future. The performance of the proposed algorithm was evaluated through comprehensive testing, and its potential real-world applications were discussed. The results of this study demonstrate the feasibility of the proposed algorithm and suggest that it has the potential to revolutionize the field of health data management. This study provides a roadmap for future work on the proposed algorithm, including its integration with encryption algorithms, performance evaluations, real-world implementation, and continuous improvement. © 2024 ieee.

关键词： Information management

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An Integrated Representation & compression Scheme Based on Convolutional Autoencoders with 4D-DCT Perceptual Encoding for High Dynamic Range Light Fields 8

An Integrated Representation & Compression Scheme Based on C...

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8th ieee International conference on Information and Communication Technology, CICT 2024

作者： Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering Chennai India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798331531539

The emerging and existing light field displays are highly capable of realistic presentation of 3D scenes on auto-stereoscopic glasses-free platforms. However, the large size of light field data presents a significant drawback for 3D displays and streaming purposes. When a light field has a high dynamic range (HDR), its size increases drastically. This paper proposes a novel compression algorithm specifically designed for high dynamic range light fields that achieves perceptually lossless compression. The algorithm leverages inter-view and intra-view correlations within the HDR light field by treating it as a four-dimensional volume. This compression technique is based on the 4D-DCT Uniform Colour Space (4DDCT-UCS) algorithm. Further encoding of 4DDCT-UCS processed images using High-Efficiency Video Coding (HEVC) eliminates intra-frame, inter-frame, and intrinsic redundancies in HDR light field data. Our comparison with state-of-the-art coders like JPEG-XL and existing HDR video coding algorithms demonstrates the superior compression performance of the proposed scheme for real-world light fields. © 2024 ieee.

关键词： Encoding (symbols)

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A Survey of Oblivious compression on Encrypted data with Fully Homomorphic Encryption 15

A Survey of Oblivious Compression on Encrypted Data with Ful...

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15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, ChanHyeok Son, Yongseok Paek, Jeongyeup Cho, Sungrae Lee, Hyung Tae School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9798350364637

Ensuring the security of private data during transmission and storage is increasingly crucial. Private information retrieval (PIR), which allows a client to retrieve specific data from a server's database without revealing any information to the server, offers a promising solution to this challenge. To design a PIR protocol, fully homomorphic encryption (FHE), which supports arbitrary computations on encrypted data, enables secure queries without exposing the queried data. However, PIR based on FHE often incurs high communication costs, leading to the development of oblivious ciphertext compression protocols that aim to reduce these costs while maintaining privacy. This paper reviews two recent techniques for oblivious ciphertext compression of encrypted data using FHE, which effectively reduce the data size transmitted between a server and a client. We examine these techniques along with their building blocks and related works in this field. © 2024 ieee.

关键词： Ciphertext

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