Theory of Quantization and Synchronization with Timing

虽然储存和传输信息的费用继续下降，但这种下降似乎只会刺激对信息的需求，从而刺激数据压缩方面的进一步发展。本研究的第一个目标是推进量化作为有损数据压缩方法的理论和实践，用于语音，图像，视频和音乐等来源。同步是数据压缩系统中的一个关键问题，在这些环境中，解码可能从压缩比特流中的任意点开始，或者压缩流可能因插入、删除或替换错误而损坏。本研究的第二个目标是开发一种数据同步理论，该理论考虑到用户需要知道数据的时间指数及其值。待开发的量化理论将增加对有损压缩系统的性能与其复杂性之间关系的定性和定量理解，其目标是设计更好的系统。该方法在很大程度上（但并非完全）基于高分辨率理论技术和概念。重点主要是矢量量化，既作为一种实用技术，也作为研究基本问题的范例。虽然同步方法已经得到了很大的发展，但除了允许解码器与编码器同步之外，还没有理论来分析和设计同步码，使其能够产生其解码数据的时间指数的估计。这项研究将发展这样一个理论。它将着重于优化编码效率（速率）、再同步延迟和定时信息产生之间的权衡。这些方法在有插入、删除和替换错误的信道编码中的应用也将被研究。

Though the cost of storing and transmitting information continues to decrease, such reductions seem only to stimulate demand for information and, consequently, for further developments in data compression. The first goal of this research is to advance the theory and practice of quantization as a lossy data compression methodology, for sources such as speech, images, video and music. Synchronization is a key issue in data compression systems that operate in environments where decoding may begin at an arbitrary point in the compressed stream of bits, or where the compressed stream may be corrupted by insertion, deletion or substitution errors. The second goal of this research is to develop a theory of data synchronizationthat takes into account the users need to know the time indices of the data, as well as its values.The quantization theory to be developed will increase the qualitative and quantitative understanding of how the performance of lossy compression systems relates to their complexity, with the goal of enabling better systems to be designed. The methodology is largely, but not exclusively, based on high resolution theory techniques and concepts. The focus is primarily on vector quantization, both as a practical technique and as a paradigm for studying fundamental issues.Though synchronization methods have been much developed, there is no theory for the analysis and design of synchronization codes that, in addition to permitting the decoder to synchronize with the encoder, enable it to produce estimates of the time indices of the data it decodes. This research will develop such a theory. It will focus on optimizing the tradeoffs among coding efficiency (rate), resynchronization delay, and the production of timing information. The application of such methods to coding for channels with insertion, deletion and substitution errors will also be investigated.