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

共 16713 条记录，以下是461-470 订阅

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Generalization of Machine Learning-Based Image compression Methods for Video compression 9

Generalization of Machine Learning-Based Image Compression M...

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9th ieee International conference on Information Technology and Nanotechnology, ITNT 2023

作者： Maksimov, Aleksey Gashnikov, Mikhail Samara National Research University Samara Russia

ISBN: (纸本)9798350397338

The article explores the adaptation of digital image compression methods based on machine learning to the case of video data compression. The generalized image compression method applies digital image generation and segmentation, pyramid-based digital image coding, and interpolation on hierarchically organized arrays of pixels based on machine learning. Image compression uses artificial convolutional neural networks and generative adversarial neural networks, super-resolution artificial neural network algorithms and autoencoders to implement the basic steps. The proposed generalization approach uses interframe dependencies to reduce information redundancy through a video frame approximator based on machine learning. Approximation can significantly reduce the entropy and variance of the encoded data, which results in a reduction in the size of data. The results of computational experiments on real video sequences prove the high efficiency of the approach proposed in this paper to generalize digital image coding methods based on machine learning for the case of video compression. © 2023 ieee.

关键词： Image compression

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Binary-Coded Ternary Number Representation in Natural Language Text compression

Binary-Coded Ternary Number Representation in Natural Langua...

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data compression conference (DCC)

作者： Zavadskyi, Igor Taras Shevchenko Natl Univ Kyiv 4d Glushkov Ave Kiev Ukraine

ISBN: (纸本)9781665478939

A lossless data compression code based on a binary-coded ternary number representation is investigated. The code is synchronizable and supports a direct search in a compressed file. A simple monotonous encoding and very fast decoding algorithms are constructed owing to code properties. Experiments show that in natural language text compression the new code outperforms byte-aligned codes SCDC and RPBC either in compression ratio or in decoding speed.

关键词： Codes Costs Natural languages data compression Encoding Robustness Entropy

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OPTIMIZING LEARNED IMAGE compression ON SCALAR AND ENTROPY-CONSTRAINT QUANTIZATION 31

OPTIMIZING LEARNED IMAGE COMPRESSION ON SCALAR AND ENTROPY-C...

引用

2024 International conference on Image Processing

作者： Borzechowski, Florian Schaefer, Michael Schwarz, Heiko Pfaff, Jonathan Marpe, Detlev Wiegand, Thomas Heinrich Hertz Inst Nachrichtentech Berlin GmbH Fraunhofer Inst Telecommun Berlin Germany Free Univ Berlin Inst Comp Sci Berlin Germany Tech Univ Berlin Dept Telecommun Syst Berlin Germany

ISBN: (纸本)9798350349405;9798350349399

The continuous improvements on image compression with variational autoencoders have lead to learned codecs competitive with conventional approaches in terms of rate-distortion efficiency. Nonetheless, taking the quantization into account during the training process remains a problem, since it produces zero derivatives almost everywhere and needs to be replaced with a differentiable approximation which allows end-to-end optimization. Though there are different methods for approximating the quantization, none of them model the quantization noise correctly and thus, result in suboptimal networks. Hence, we propose an additional finetuning training step: After conventional end-to-end training, parts of the network are retrained on quantized latents obtained at the inference stage. For entropy-constraint quantizers like Trellis-Coded Quantization, the impact of the quantizer is particularly difficult to approximate by rounding or adding noise as the quantized latents are interdependently chosen through a trellis search based on both the entropy model and a distortion measure. We show that retraining on correctly quantized data consistently yields additional coding gain for both uniform scalar and especially for entropy-constraint quantization, without increasing inference complexity. For the Kodak test set, we obtain average savings between 1% and 2%, and for the TecNick test set up to 2.2% in terms of Bjontegaard-Delta bitrate.

关键词： Deep-Learning Image compression Quantization Rate-Distortion-Optimization Trellis-Coded-Quantization

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Enhanced Federated Reinforcement Learning for Mobility-Aware Node Selection and Model compression

Enhanced Federated Reinforcement Learning for Mobility-Aware...

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ieee conference on Global Communications (ieee GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Hu, Bingxu Huang, Xiaoyan Zhang, Ke Wu, Fan Sun, Chen Cui, Tao Zhang, Yan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China RDC China Sony China Ltd Beijing Peoples R China Univ Oslo Dept Informat Oslo Norway

ISBN: (纸本)9798350310900

Federated Learning (FL) is an emerging distributed learning architecture that allows multiple agents to share knowledge in machine learning. However, in the scenario with mobile agents, the mobility of agents significantly affects the learning performance. Besides, frequent exchange of local models could result in high communication overhead. In this paper, we propose a Mobility-aware Federated Reinforcement Learning (MFRL) framework. In MFRL, we model the influences of agent mobility on communication quality and data correlation, and devise a mobility-aware node selection algorithm, so as to accelerate the training procedure and improve the learning performance, taking learning quality, wireless channel quality, and data correlation of agents into consideration. A knowledge distillation (KD) based model compression method is integrated into the MFRL to reduce the communication overhead as well as accelerate the inference process. Finally, taking deep reinforcement learning (DRL) based collision avoidance of intelligent vehicles as a study case, the effectiveness of MFRL is verified. Numerical results demonstrate that the proposed MFRL can accelerate the training process and improve the learning performance.

关键词： Federated reinforcement learning mobility awareness model compression

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Optimizing data Transmission in IoT Networks through Enhanced compression and Edge Computing Techniques 6

Optimizing Data Transmission in IoT Networks through Enhance...

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6th ieee International conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2023

作者： Serhii, Ushakov Vasyl, Kurdecha National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9798350348484

As the Internet of Things (IoT) continues to evolve, ensuring efficient data compression and transmission emerges as a pivotal challenge, especially considering the energy constraints and data volume transmitted across the network. This work introduces an innovative approach for enhancing data compression in IoT systems, leveraging algorithms that efficiently utilize edge technologies for data distribution and transmission, coupled with data compression via a proficiently trained neural network. The primary aim of this methodology is to curtail the data transmission volume within the IoT network, which consequentially amplifies data transmission speeds and minimizes transmission costs. Furthermore, the proposed distributed system, operative on edge devices, ensures system scalability while mitigating the probability of data transmission errors, concurrently augmenting data security within the IoT network. Through meticulous analysis and application of IoT data compression methods, we have refined the data transmission process, utilizing a distinctive blend of classification and distribution. The ensuing software, derived from the proposed method, renders specialized data compression for the IoT network. Preliminary results indicate that amalgamating the neural network method of data compression with classical methods on edge devices markedly elevates data transmission efficiency, offering a viable pathway to balancing energy efficiency, transmission costs, and security in IoT networks. © 2023 ieee.

关键词： Energy efficiency

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Combining Lossy compression with Multi-level Caching for data Staging over Network

Combining Lossy Compression with Multi-level Caching for Dat...

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1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Aoyagi, Rei Takahashi, Keichi Shimomura, Yoichi Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ Cybersci Ctr Sendai Miyagi Japan

ISBN: (纸本)9798350364613;9798350364606

Researchers conduct post-processing on the simulation results by running an interactive data analysis tool on a High-Performance Computing (HPC) system installed at an HPC center and retrieving the post-processed results. Certain data analysis scenarios require to transfer the simulation results directly from the center. in such scenarios, a portion of the data would usually be streamed over the network to achieve interactivity. However, there still exist two challenges in maintaining interactivity: (1) limited network bandwidth and (2) long network latency. To tackle these challenges, we propose a system to enable interactive array analysis over the network. We employ error-bounded lossy compression to increase the effective network bandwidth. Furthermore, we employ multi-level caching to hide the network latency and combine prefetching to improve the cache hit ratio. The cache replacement and prefetching policies are designed considering the data access pattern of interactive analysis. We compared our proposed system with TileDB, one of the state-of-the-art array databases, by measuring the average latency for various access patterns. Compared to TileDB, the proposed system reduces the average latency by up to 91.6% by allowing 10% of error because the cache hit ratio was improved by more than 40% due to the proper cache replacement and prefetching policy and network transfer time was reduced more than 75% by using lossy compression.

关键词： HPC Distributed Computing data Staging Lossy compression Interactive Analysis Bandwidth Software Caching

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Research on Demand Response data compression Based on Neural Network under 5G Environment 5

Research on Demand Response Data Compression Based on Neural...

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5th ieee International conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Pan, Yuqing Qi, Bing Li, Bin Zhu, Enguo Liu, Yan Lu, Chunguang Wang, Chaoliang School of Electrical and Electronic Engineering North China Electric Power University Beijing China China Electric Power Research Institute Beijing China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

ISBN: (纸本)9798350333442

In the context of 5G, data volume is exploding, and data transmission and processing are facing challenges. In this paper, a data compression algorithm based on neural network is proposed to solve the problem of large scale and high dimension of demand response data in the process of transmission and storage. By mapping demand response data to low dimensional space for representation, the algorithm effectively reduces the amount of data and improves the efficiency of data transmission and storage. The algorithm uses Long Short Term Memory network for data compression and restoration. The experimental results show that the data compression algorithm based on neural network has good compression effect and accuracy. At the same time, the algorithm has good versatility and adaptability, and can be applied to the compression and restoration of data sets of different types and sizes, providing an effective solution for efficient demand response data processing. © 2023 ieee.

关键词： Restoration

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Toward Parallelization Technique for Stream-based Lossless data compression

Toward Parallelization Technique for Stream-based Lossless D...

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2023 ieee International conference on Big data, Bigdata 2023

作者： Kato, Taiki Yamagiwa, Shinichi Wada, Koichi Master's Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Ibalaki Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Ibalaki Tsukuba Japan

ISBN: (纸本)9798350324457

IoT edge platform has become popular in various distributed environments. The edge devices need to communicate Bigdata among them or with the cloud servers by collaborating with AI technologies for finding events from the applications. Those devices exchange data streams from such as distributed sensors and remote image/video devices. We focus on an acceleration technique for the communication performance using a stream-based lossless data compression technology. This paper proposes a parallelization technique for the compression process in a software environment running on a multicore processor. The technique invokes concurrent compression processes assigned to multiple threads with splitting a data stream to chunks. The paper exposes three scheduling methods for assigning the chunks to the threads: in-order, hybrid and out-of-order. As an original data order of chunks must be obtained in decompression side, the proposed technique introduces packeting mechanisms in each chunk by adding headers to support the scheduling methods. Through experimental performance evaluations, we discuss the packeting overhead focusing on compression ratio and speedup by the parallelization with three scheduling methods. © 2023 ieee.

关键词： data Stream IoT Lossless data compression Multi-Threading Parallelization

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Enhanced Energy Efficiency in Visual Sensor Networks through ROI-Based compression Techniques in Wildlife Surveillance 12

Enhanced Energy Efficiency in Visual Sensor Networks through...

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2024 12th International conference on Systems and Control, ICSC 2024

作者： Hadji, Oussama Maimour, Moufida Kadri, Ouahab Benyahia, Abderrezak Rondeau, Eric University of Batna 2 LASTIC Laboratory Dept. Computer Science Batna Algeria Universite de Lorraine CNRS CRAN NancyF-54000 France

ISBN: (纸本)9798331531812

This paper presents an application of a Region of Interest(ROI)-based compression technique designed to enhance the energy efficiency of visual sensor networks used in wildlife monitoring. By focusing on compressing only the most critical regions within each video frame, the proposed method significantly reduces data volume, leading to substantial energy savings during both compression and transmission stages. The integration of LoRaWAN technology further optimizes energy consumption by providing low-power, long-range communication capabilities. Experimental results demonstrate a compression ratio of 4:1, achieving overall energy savings of approximately 38% for short-range and 40% for long-range transmission compared to traditional non-ROI methods. Despite a slight reduction in image quality, the visual integrity remains acceptable for effective wildlife monitoring, and the method improves transmission success rates over varying distances. These findings highlight the potential of ROI-based compression to extend the operational lifespan of sensor nodes, offering a viable and sustainable solution for long-term environmental monitoring. © 2024 ieee.

关键词： Energy saving

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Efficient Lossy Satellite Image compression using Hybrid Autoencoder Model 14

Efficient Lossy Satellite Image Compression using Hybrid Aut...

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14th International conference on Electrical Engineering (ICEENG)

作者： Badr, Mohamed A. Elrewainy, Ahmed F. Elshafey, Mohamed A. Mil Tech Coll Avion Engn Dept Cairo Egypt Mil Tech Coll Comp Engn & AI Dept Cairo Egypt

ISBN: (纸本)9798350343427;9798350343434

The challenge of handling the vast amount of data generated by onboard satellite instruments arises due to limited bandwidth and memory capacity, which must be managed cautiously. Image compression is used as a tool to minimize the data size for easier storage and transmission between ground stations. An efficient compression method maintains the quality of the source representation in the reconstruction stage. Conventional lossy image compression techniques have been used for the last few decades. However, recent learning-based approaches have gained substantial interest in the artificial intelligence field of research that achieves highly promising image reconstruction results under adequate storage, and low bandwidth. In this paper, a hybrid approach was proposed, a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) autoencoder to improve compression efficiency. The proposed model was applied to the RGB benchmark satellite images dataset (EuroSat dataset). The experiment outcomes indicate that the proposed solution surpasses the Joint Photographic Experts Group (JPEG) family in regards to reconstruction evaluation criteria Peak Signal Noise Ratio (PSNR) and Structural Similarity Index (SSIM), with an improvement up to 34.7% and 6.36% respectively. Additionally, it outperforms the most recent deep learning approach, by 3.63% in terms of PSNR and 0.43% in terms of SSIM.

关键词： Image compression Deep Learning Autoencoders Recurrent Neural Network Convolutional Neural Network

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