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

共 16713 条记录，以下是471-480 订阅

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LEVERAGING REDUNDANCY IN FEATURE FOR EFFICIENT LEARNED IMAGE compression 49

LEVERAGING REDUNDANCY IN FEATURE FOR EFFICIENT LEARNED IMAGE...

引用

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

作者： Qin, Peng Bao, Youneng Meng, Fanyang Tan, Wen Li, Chao Wang, Genhong Liang, Yongsheng Harbin Inst Technol Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

In recent years, with the development of the field of learned image compression, numerous models with excellent ratedistortion performance have emerged. However, the considerable computational complexity inherent in these models poses challenges for their practical deployment. In this paper, we investigate feature redundancy in learned image compression (LIC) algorithms for efficient feature extraction and introduce an efficient and lightweight LIC framework. Specifically, we explore the existence of a large number of similar features in the network. Subsequently, we design effective feature extraction modules across various levels, such as layer and block. In addition, based on the fact that the role of the codec's encoder is to remove redundancy and the decoder is to reconstruct, we propose an asynchronous feature fusion block. This fusion block incorporates an "edge smoothing" operator in the encoder and an "edge enhancement" operator in the decoder. Our methodology strikes an ideal balance between rate-distortion performance and efficiency. The experimental results indicate that our approach necessitates only 310KMac/pixel computation and 9.5M parameters, while in terms of performance, our method achieves a 20.7% BD-rate advantage over BPG on Kodak data, mirroring VVC's performance. Compared to other learned image compression algorithms with SOTA performance, our method has a great advantage in terms of computation/parameter count.

关键词： Learned image coding neural network lightweight convolution

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AutoMC: Automated Model compression Based on Domain Knowledge and Progressive Search 40

AutoMC: Automated Model Compression Based on Domain Knowledg...

引用

40th ieee International conference on data Engineering, ICDE 2024

作者： Wang, Chunnan Wang, Hongzhi Shi, Xiangyu Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350317152

Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource constrained environments. Despite their great success, the selection of suitable compression methods and design of details of the compression scheme are difficult, requiring lots of domain knowledge as support, which is not friendly to non-expert users. To make more users easily access to the model compression scheme that best meet their needs, in this paper, we propose AutoMC, an effective and efficient automatic tool for model compression. In order to improve the search efficiency and quality, in AutoMC, we build the domain knowledge on model compression to deeply understand the characteristics and advantages of each compression method under different settings. This method can provide AutoMC with the more reasonable guidance and thus reduce useless evaluation. In addition, we present a progressive search strategy to efficiently explore pareto optimal compression scheme according to the learned prior knowledge combined with the historical evaluation information. This strategy can help AutoMC selectively and gradually explore more valuable search space, and thus reduce the search difficulty and improve the search efficiency. Extensive experimental results show that AutoMC can provide users with better compression schemes within short time compared to the existing compression methods and AutoML algorithms, which demonstrates the effectiveness and significance of our proposed algorithm. © 2024 ieee.

关键词： Domain Knowledge

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Image compression-then-encryption model based on A1 attractor

Image compression-then-encryption model based on A1 attracto...

引用

2024 International conference Automatics and Informatics, ICAI 2024

作者： Ivanova, Tsvetelina Dobrev, Dimitar Stoyanov, Borislav Ribarski, Plamen University of Shumen Department of Computer Informatics Shumen Bulgaria

ISBN: (纸本)9798350353907

Confidential data may be hacked in a number of ways, thus diversity protection strategies are needed to ensure the security of message transferred online. In order to increase date transmission confidentially, we will provide a secure image compression-then-encryption model in this study based on an A1 chaotic attractor. The procedure is put through several security checks. Robust examination demonstrates that the approach with enhanced features outperforms other comparable image encryption models. © 2024 ieee.

关键词： data compression

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NERI: IMPLICIT NEURAL REPRESENTATION OF LIDAR POINT CLOUD USING RANGE IMAGE SEQUENCE 49

NERI: IMPLICIT NEURAL REPRESENTATION OF LIDAR POINT CLOUD US...

引用

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

作者： Xue, Ruixiang Li, Jiaxin Chen, Tong Ding, Dandan Cao, Xun Ma, Zhan Nanjing Univ Nanjing Peoples R China Hangzhou Normal Univ Hangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

This paper proposes the NeRI, an implicit neural representation (INR) based LiDAR point cloud compressor. In NeRI, we first transform a sequence of 3D LiDAR frames into a 2D range image sequence through range image projection over time. Then, we employ a neural network conditioned on the temporal frame index and associated LiDAR sensor pose to fit input range images as closely as possible. The optimized network parameters, which implicitly represent the input LiDAR data, are later lossily compressed. NeRI decoder is then initialized using decoded parameters to generate range images for reconstructing the 3D LiDAR sequence accordingly. Extensive experimental results demonstrate the significant superiority of NeRI regarding the compression efficiency and decoding speed compared to state-of-the-art 2D and 3D compressors for LiDAR point cloud.

关键词： Point cloud geometry compression LiDAR data implicit neural representation range image

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Development of data compression Techniques for IoT data Transmission in Isolated Aquaculture Using LoRa Through LoRaWAN Gateway Antares Telkom 18

Development of Data Compression Techniques for IoT Data Tran...

引用

18th International conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Khaderani, Helmi Ahmad Agusdian, Aldo Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331527686

Aquaculture in remote areas challenges significant monitoring due to limited internet access. This research develops and implements an IoT communication system using LoRa RFM95W module, ESP32 microcontroller, and Telkom's LoRaWAN Gateway Antares for efficient and reliable sensor data transmission. LoRa operates in the 920-923 MHz band which offers long-range communication with low power consumption and is ideal for remote IoT applications. Class A LoRaWANare selected for power efficiency and an optimal Spreading Factor (SF) is chosen to balance coverage, data rate and power consumption. data compression techniques are applied to minimize data loss, reduce data size, optimize bandwidth usage and ensure real-time data integration. Experiments with various compression algorithms and distances showed that the unishox2 algorithm achieved the best compression ratio, capable of transmitting data sizes of up to 347 bytes with minimal signal loss on SF9. © 2024 ieee.

关键词： Aquaculture

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Wavelet-Based data compression for Remote Sensing and Image Processing

Wavelet-Based Data Compression for Remote Sensing and Image ...

引用

2024 International conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Dhyan Chandra Dadhich, Atul Saha, Ananya Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. Wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously. © 2024 ieee.

关键词： Best compression data Ejficaciously Lack Processing Television Time

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Novel Dual-Encryption and compression Strategy: AES and Fernet for Secure Multimedia Transmission 5

Novel Dual-Encryption and Compression Strategy: AES and Fern...

引用

5th International conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Zala, Dhruvi Karansinh Akram Khan, Mohammad Madhav University Faculty of Engineering and Technology Rajasthan 307026 India

ISBN: (纸本)9798331518097

The increasing variety of multimedia data types, such as text, images, and audio, has necessitated advanced data encryption techniques to ensure secure transmission and storage. Traditional encryption methods, including AES and Fernet, have shown limitations in meeting the demands of modern multimedia security. This research focused on developing an enhanced encryption system by integrating AES and Fernet encryption algorithms with the zlib compression technique. The proposed system provided an efficient solution for encrypting and decrypting diverse data formats, offering both improved security and enhanced processing speed. Key outcomes included stronger safety and security measures alongside better compression ratios. This approach aimed to establish a robust and adaptive security framework capable of addressing the evolving requirements for protecting multimedia data and ensuring the integrity of digital information. © 2024 ieee.

关键词： Advanced Encryption Standard (AES) compression Encryption Fernet Hybrid Approach Multimedia data Security

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Dynamic Slope Detection: A High-compression Fidelity-Preserving Approach for ECG Signal Acquisition 67

Dynamic Slope Detection: A High-Compression Fidelity-Preserv...

引用

67th ieee International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Saenz-Noval, Jorge J. Lenero-Bardallo, Juan Antonio Gontard, Lionel C. Tang, Wei Univ Cadiz Dept Condensed Matter Phys Cadiz 11519 Spain Univ Cadiz IMEYMAT Cadiz 11519 Spain Univ Seville Inst Microelect Sevilla IMSE CNM CSIC Seville Spain New Mexico State Univ Klipsh Sch Elect & Comp Engn Las Cruces NM 88003 USA

ISBN: (纸本)9798350387186;9798350387179

This paper introduces a novel Dynamic Slope Detection (DSD) system for acquiring electrocardiogram (ECG) signals. DSD addresses the critical challenge of balancing data storage requirements with signal fidelity, particularly in resource-constrained environments like wearable devices. The system leverages the slope information of the ECG signal to guide efficient and adaptive data sampling. Validation using ten samples from the publicly available MIT-BIH Arrhythmia database confirmed significant data reduction compared to traditional sampling. The proposed method achieves a compression ratio of up to 12.5x while maintaining RR interval estimation error below +/- 0.1 msec.

关键词： Electrocardiogram (ECG) analog-to-digital converter (ADC) low-power circuits slope level-crossing sampling wearable devices

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UGC: Unified GAN compression for Efficient Image-to-Image Translation

UGC: Unified GAN Compression for Efficient Image-to-Image Tr...

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ieee/CVF International conference on Computer Vision (ICCV)

作者： Ren, Yuxi Wu, Jie Zhang, Peng Zhang, Manlin Xiao, Xuefeng He, Qian Wang, Rui Zheng, Min Pan, Xin ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350307184

Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-toimage translation. However, the success of these GAN models hinges on ponderous computational costs and laborexpensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs;ii) data/labelefficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model. Extensive experiments demonstrate that UGC obtains state-of-the-art lightweight models even with less than 50% labels. UGC that compresses 40x MACs can achieve 21.43 FID on *** with 25% labels, which even outperforms the original model with 100% labels by 2.75 FID.

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Enhanced Vehicle Detection by Optimized Image compression in NextG Wireless Network Autonomous Vehicles System 100

Enhanced Vehicle Detection by Optimized Image Compression in...

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100th ieee Vehicular Technology conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN. © 2024 ieee.

关键词： Radio access networks

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