用于视频成像的压缩比学习和语义通信

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-03-27 DOI:10.1109/JSTSP.2024.3405853
Bowen Zhang;Zhijin Qin;Geoffrey Ye Li
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引用次数: 0

摘要

对于电力、内存和带宽资源有限的移动机器人来说,提高数据采集和传输效率至关重要。为了提高数据采集效率,我们设计了一种新型视频压缩传感系统,该系统具有空间变异压缩比,能以较低的采样率提供较高的成像质量;为了提高数据传输效率,我们利用语义通信降低带宽需求,能以较低的传输速率提供较高的图像复原质量。我们尤其关注速率与质量之间的权衡。为了应对这一挑战,我们使用神经网络来决定给定质量要求下的最优速率分配策略。由于速率问题不可区分,我们通过基于策略梯度的强化学习来训练网络。数值结果表明,所提出的方法优于现有的基线方法。
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Compression Ratio Learning and Semantic Communications for Video Imaging
It is crucial to improve data acquisition and transmission efficiency for mobile robots with limited power, memory, and bandwidth resources. For efficient data acquisition, a novel video compressed-sensing system with spatially-variant compression ratios is designed, which offers high imaging quality with low sampling rates; To improve data transmission efficiency, semantic communication is leveraged to reduce bandwidth requirement, which provides high image recovery quality with low transmission rates. In particular, we focus on the trade-off between rate and quality. To address the challenge, we use neural networks to decide the optimal rate allocation policy for given quality requirements. Due to the non-differentiable issue of rate, we train the networks by policy-gradient-based reinforcement learning. Numerical results show the superiority of the proposed methods over the existing baselines.
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
期刊最新文献
Front Cover Table of Contents IEEE Signal Processing Society Information Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications IEEE Signal Processing Society Information
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