Coding for Multimedia Sources and Sensor Data: New Theory, Algorithms, and Applications

多媒体源和传感器数据编码：新理论、算法和应用

• 批准号: 0515109
• 负责人: Gregory Wornell
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0515109&HistoricalAwards=false
• 关键词: Coding; Multimedia; Sources; Sensor; Data

ABSTRACTSource coding plays a critical role in many modern digital systems.Indeed, as a research area, source coding is more important andfertile than ever, with a wealth of new opportunities and challengesarising from the increasing pervasiveness of multimedia and sensordata in a digital world. This research pursues a set of particularlyinteresting, novel, and timely directions in this realm, and aims toimpact the design of practical systems. There are three complementaryaspects to the research: theory, algorithms, and applications.In the theory area, the emphasis is on problems of distributed sourcecoding. There has been strong growth in interest in such problems,motivated by, for example, the need for resource efficientquantization and compression techniques in networks. Theinvestigation pursues a substantial but largely unexplored aspect ofdistributed source coding theory: how distortion side information--- i.e., information about the relative importance of the signalcomponents to be coded --- can be exploited in such problems.In the algorithms area, the focusses on turbo-quantization ---low-complexity iterative source encoding techniques and theirimplementation. Over the past decade, there has been a tremendoussuccess in developing low-complexity iterative channel decodingalgorithms for use with error-correction codes. The correspondingsource codes obtained via duality principles have the potential tosimilarly transform the practice of source coding.Finally, the research investigates important emergingdecision-system applications in which source coding anddistortion criteria play an integral role, but in nontraditional ways.Specific examples being pursued include hashing systems formultimedia databases and systems for authenticating multimediacontent.

信源编码在现代数字系统中起着至关重要的作用，在数字世界中，多媒体和传感器数据的日益普及带来了大量的新机遇和挑战，信源编码作为一个研究领域比以往任何时候都更加重要和丰富。 本研究在这一领域寻求一系列特别有趣、新颖和及时的方向，旨在影响实际系统的设计。 本文从理论、算法和应用三个方面进行研究，在理论方面，重点研究分布式源代码编码问题。 由于对网络中资源有效的量化和压缩技术的需求，人们对这类问题的兴趣日益浓厚。 调查追求一个实质性的，但在很大程度上未探索的方面分布式信源编码理论：如何失真边信息-即，在算法方面，重点讨论了Turbo量化--一种低复杂度的迭代信源编码技术及其实现。 在过去的十年中，在开发用于纠错码的低复杂度迭代信道译码算法方面取得了巨大的成功。 通过对偶原理获得的相应的源代码有可能类似地改变源代码的实践。最后，研究调查重要的新兴决策系统的应用，其中源代码和失真标准发挥不可或缺的作用，但在非传统的方式。正在追求的具体例子包括散列系统formultimedia数据库和系统认证多媒体内容。