摘要翻译：
隐马尔可夫信源(HMS)除了在语音处理与识别、图像理解和传感器网络中的显式建模外，还代表了一个广泛的通过噪声捕获过程获得的实用信源，本文研究了HMS的最优编码。在跟踪状态概率分布估计的基础上，提出了一种新的HMS基本信源编码方法，并证明了该方法是最优的。介绍了利用主要概念的实用编码器和解码器方案。提出了一种迭代方法来优化系统。它还着重于最优HMS量化范式的一个重要扩展。提出了一种新的HMS可伸缩编码方法，该方法在对给定层进行编码时，考虑了所有可用的信息。仿真结果表明，这些方法显著降低了重构失真，并大大优于现有技术。
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英文标题：
《Layered Coding of Hidden Markov Sources》
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作者：
Mehdi Salehifar, Tejaswi Nanjundaswamy and Kenneth Rose
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The paper studies optimal coding of hidden Markov sources (HMS), which represent a broad class of practical sources obtained through noisy acquisition processes, beside their explicit modeling use in speech processing and recognition, image understanding and sensor networks. A new fundamental source coding approach for HMS is proposed, based on tracking an estimate of the state probability distribution, and is shown to be optimal. Practical encoder and decoder schemes that leverage the main concepts are introduced. An iterative approach is developed for optimizing the system. It also focuses on a significant extension of the optimal HMS quantization paradigm. It proposes a new approach for scalable coding of HMS which accounts for all the available information while coding a given layer. Simulation results confirm that these approaches significantly reduce the reconstructed distortion and substantially outperform existing techniques.
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PDF链接：
https://arxiv.org/pdf/1802.02709