Adaptive Signal Decompositions with Applications in Compression

自适应信号分解及其在压缩中的应用

• 批准号: 9321302
• 负责人: Jean Walrand
• 金额: $ 26.11万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-04-01 至 1998-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9321302&HistoricalAwards=false
• 关键词: Adaptive; Signal; Decompositions; Applications; Compression

This research explores adaptive methods for high performance, lossy signal compression, and in particular, focuses on a variety of methods that rely on signal expansions. Among these are: (1) signal adaptive expansions that include quantization and entropy coding. These are generalizations of transform or subband/wavelet and wavelet packet schemes. In particular, adaptive wavelet packets are being studied, both from the point of a view of constructing bases and that of finding good and efficient algorithms to find the best bases. (2) Overcomplete expansions or frames. The focus is on quantization performance and computational complexity. (3) The class of compression algorithms that use successive approximation. A rage-distortion version of matching pursuit is being developed; generalizations of hierarchical methods based on wavelets and wavelet packers are being investigated. (4) Adaptive schemes for compression, including adaptive transforms and quantization. Goals include a lossy equivalent of arithmetic coding, and a lossy dictionary based predictive compression scheme that resembles a lossy Lempel-Ziv algorithm.

本研究探讨了高性能，有损信号压缩的自适应方法，特别是，侧重于各种依赖于信号扩展的方法。 其中包括：（1）信号自适应扩展，包括量化和熵编码。 这些是变换或子带/小波和小波包方案的概括。 特别是，自适应小波包正在研究，无论是从一个角度来看，建设基地，并找到良好的和有效的算法，以找到最好的基地。 (2)过度完整的扩展或框架。 重点是量化性能和计算复杂度。 (3)使用逐次逼近的一类压缩算法。 一个匹配追踪的失真版本正在开发中;正在研究基于小波和小波包的分层方法的推广。 (4)压缩的自适应方案，包括自适应变换和量化。 目标包括算术编码的有损等价物，以及类似于有损Lempel-Ziv算法的基于有损字典的预测压缩方案。