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

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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.