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

共 16713 条记录，以下是91-100 订阅

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Image compression Algorithms - Comparative Analysis

Image Compression Algorithms - Comparative Analysis

引用

Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： G. Niharika Reddy M. Namrutha Namratha Akshaya Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive Coding, Run Length Coding and Quantum Entropy Coding (QEC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming.

关键词： Integrated circuits Image quality Image coding Social networking (online) Streaming media Predictive coding Media Predictive models Prediction algorithms Entropy coding

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Two-stage Reinforcement Learning Empowered Wireless Semantic Video Transmission

Two-stage Reinforcement Learning Empowered Wireless Semantic...

引用

ieee conference on Wireless Communications and Networking

作者： Mujian Zeng Wenbo Ma Kaipeng Zheng Wenqin Zhuang Mingkai Chen

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rapid development of video conferencing technology, interactive multimedia services proliferate, resulting in a surge in business traffic. Remote work and online collaboration will become the mainstream office in the future, which poses new challenges to computer vision technology in the field of communication. At the same time, the appearance of semantic communication has solved the problem that the traditional video coding technology can not deal with semantic redundancy. Therefore, we propose the Semantic Transmission Optimization of Video conference(STOVC) architecture to meet the interactivity requirements of multimedia services. We first use Iterated Integrated Attribution(IIA) and Segment Anything Model 2(SAM2) to understand the video and complete the semantic segmentation of the video. We then propose Neural Video compression with Actor-Critic(NVCAC) to optimize video encoding and transmission strategies to balance video compression with semantic fidelity. Finally, the $\alpha$ -fusion method is used to reconstruct the video at the receiving end. In addition, we use DeepCache accelerated stable diffusion to generate conference video to enrich the data set. The experimental results show that STOVC has a better performance than H.264 and H.265 at low bit rate.

关键词： Wireless communication Video coding Computer architecture Reinforcement learning Video compression Streaming media Semantic communication Encoding Optimization Videoconferences

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All-in-One Image compression and Restoration

All-in-One Image Compression and Restoration

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Huimin Zeng Jiacheng Li Ziqiang Zheng Zhiwei Xiong University of Science and Technology of China The Hong Kong University of Science and Technology

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual images corrupted by various types and levels of degradations are commonly encountered in practical image compression. However, most existing image compression methods are tailored for clean images, therefore struggling to achieve satisfying results on these images. Joint compression and restoration methods typically focus on a single type of degradation and fail to address a variety of degradations in practice. To this end, we propose a unified framework for all-in-one image compression and restoration, which incorporates the image restoration capability against various degradations into the process of image compression. The key challenges involve distinguishing authentic image content from degradations, and flexibly eliminating various degradations without prior knowledge. Specifically, the proposed framework approaches these challenges from two perspectives: i.e., content information aggregation, and degradation representation aggregation. Extensive experiments demonstrate the following merits of our model: 1) superior rate-distortion (RD) performance on various degraded inputs while preserving the performance on clean data; 2) strong generalization ability to real-world and unseen scenarios; 3) higher computing efficiency over compared methods. Our code is available at https://***/ZeldaM1/All-in-one.

关键词： Degradation Visualization Computer vision Image coding Codes Codecs Computational modeling Rate-distortion data models Image restoration

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Attention-Infused Autoencoder for Massive MIMO CSI compression

Attention-Infused Autoencoder for Massive MIMO CSI Compressi...

引用

ieee conference on Wireless Communications and Networking

作者： Kangzhi Lou Han Ji Xiping Wu School of Electrical and Electronic Engineering University College Dublin Dublin Ireland The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The ever growing number of antennas in massive multiple-input multiple-output (MIMO) systems have significantly burdened the demand for the feedback of channel state information (CSI). This drives the research on CSI compression for massive MIMO. Recent development of autoencoder-based methods has proven to break the limit of conventional data compression methods in terms of both accuracy and computational complexity. However, like conventional methods, those autoencoder-based methods are trained for certain channel scenarios (such as indoor and outdoor) and would require a dedicated model for each scenario, limiting the practicability. It is challenging to develop a unified model to compress CSI across different channel scenarios, as their properties are diverse. In this paper, such a model is proposed for the first time, which is named attention-infused autoencoder network (AiANet). A dual attention mechanism is developed to capture the spatial and channel features of distinctive CSI. Multi-resolution convolutions are also employed to enhance the ability of feature extraction. Simulation results demonstrate that AiANet can substantially outperform ACRNet, which is a state-of-the-art auto encoder model. In terms of normalized mean squared error (NMSE), the proposed method can achieve an improvement of up to 3.69 dB in indoor and 1.26 dB in outdoor.

关键词： Attention mechanisms Limiting Computational modeling Simulation Autoencoders data compression Massive MIMO Feature extraction Computational complexity Channel state information

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Image compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication

Image Compression Algorithm Based on Region of Interest Extr...

引用

ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Yanxia Liang Tong Jia Xin Liu Huanhuan Zhang Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China College of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak Signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications.

关键词： Image segmentation Visualization Image coding PSNR Wireless networks Bandwidth Autonomous aerial vehicles Real-time systems Image reconstruction Videos

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MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

引用

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

作者： ZhaoYi Jiang Dong Han Cao Song FangZhe Nan Bailin Yang School of Computer Science and Technolgy Zhejiang Gongshang University School of Statistics and Mathematics Zhejiang Gongshang University Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2).

关键词： Point cloud compression Costs Accuracy Dynamics Termination of employment Signal processing Real-time systems Acoustics Trajectory Speech processing

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Quantum Run-length Encoding: Optimizing data compression on Quantum Computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

引用

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

作者： Jiale Zhang Xilong Che Shiyong Jin Kaifan Pan Shun Peng Juncheng Hu College of Computer Science and Technology Jilin University Changchun China College of Intelligent Science and Engineering Harbin Engineering University Harbin China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.

关键词： Computers Training Quantum computing Image coding Runtime Computational modeling Qubit data compression Hardware Complexity theory

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Design and Implementation of a Highly Efficient LIDAR-Based Teleoperated Driving System

Design and Implementation of a Highly Efficient LIDAR-Based ...

引用

Consumer Communications and Networking conference, CCNC ieee

作者： Johann Nikolai Hark Julian Pfeifer Jorin Kouril Bernd Schäufele Ilja Radusch Daimler Center for Automotive Information Technology Innovations (DCAITI) Berlin Germany Fraunhofer Institute for Open Communication Technologies (FOKUS) Berlin Germany

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Automated driving has advanced significantly, but challenges remain in scenarios like urban areas and parking garages due to technical and legal constraints. As automated vehicles rise, connected vehicle technologies gain focus. Tele-operated driving serves as a bridge technology for situations where automated driving is limited. This paper presents a novel teleoperated driving system to extend the Operational Design Domain of automated vehicles, such as in underground parking, using Direct Short Range Communication (ieee 802.11p). The approach emphasizes efficient sensor data transmission from LIDAR, preprocessing raw data to filter point clouds and detect freespace and objects, enhancing responsiveness and reliability.

关键词： Point cloud compression Bridges Laser radar Connected vehicles Law Urban areas Reliability data communication

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AJPEG: A 26.4-pJ/pixel, 252-fps, 128×128 Image Sensor with an In-Sensor Analog DCT Processor for data compression

AJPEG: A 26.4-pJ/pixel, 252-fps, 128×128 Image Sensor with ...

引用

Custom Integrated Circuits conference (CICC)

作者： Rentao Wan Yichen Xu Dong-Woo Jee Mingoo Seok Columbia University New York US Ajou University Suwon Korea

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

An image sensor is one of the most important building blocks of modern electronics, such as smartphones, drones, and autonomous vehicles. The state-of-the-art image sensor captures an incredibly detailed, high-resolution image [1]–[3], however, it also generates a large amount of data, which overwhelms the downstream processing. To address this challenge, recent works have investigated in- and near-sensor data compression techniques [4]–[14]. [4] is one of the few works which demonstrated the end-to-end systems (containing pixels, compression hardware, and analog-to-digital converters [ADC]). However, it consumes a significant amount of energy of 5919 pJ/pixel, of which the analog discrete cosine transform (DCT) processor takes 58%. On the other hand, [5] proposed the analog compressor and ADC systems (no pixels). However, it also consumes a non-negligible amount of energy (404 pJ/pixel) and exhibits a limited performance of only 6 frame-per-second (fps). In contrast, [6], [7] proposed digital DCT processors, which reduce the energy consumption down to 29–126 pJ/pixel. However, they require the analog-digital conversion of every pixel, which would significantly increase the overall energy consumption.

关键词： Image sensors Hands Energy consumption Image coding data compression Hardware Discrete cosine transforms Analog-digital conversion Smart phones Drones

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On-the-fly Image compression Using Overfitted Shallow CNN Autoencoders

On-the-fly Image Compression Using Overfitted Shallow CNN Au...

引用

International Image Processing, Applications and Systems conference (IPAS)

作者： John El Gallab Abdelwahab Ganna Abdallah Kassem Hend Makram Martina Muawad Mina Mikhael Tamer ElBatt Computer Science and Engineering The American University in Cairo Cairo Egypt

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In the era of pervasive Internet use, managing large volumes of image data becomes crucial. To mitigate storage and bandwidth costs, image compression plays a pivotal role. Traditional image compression techniques like JPEG and PNG, while widely used, may suffer from limited compression rates. In this work, we propose a novel approach using shallow Convolutional Neural Network (CNN) autoencoders for on-the-fly image compression. Our model aims to achieve high compression rates with improved image quality compared to classical methods. Additionally, it supports decompression on the CPU in realtime, making no assumptions about client-side computational resources. We present a comprehensive methodology, including architecture design, experiments, and performance metrics. Our results demonstrate the effectiveness of the proposed approach, providing a competitive alternative for online content compression and decompression that outperforms JPEG in image quality assessment metrics at 33% compression rate. We also outperform other Learned Image compression (LIC) techniques in both the decompression time and number of trainable parameters with an improvement in the order of 10-times.

关键词： Measurement Image quality Image coding Autoencoders Transform coding Bandwidth Internet Convolutional neural networks Time factors Image reconstruction

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