Adaptive Estimation in Wavelet Image Compression: Thresholding and Quantization

小波图像压缩中的自适应估计：阈值化和量化

• 批准号: 9802314
• 负责人: Bin Yu
• 金额: $ 7.5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-15 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9802314&HistoricalAwards=false
• 关键词: Adaptive; Estimation; Wavelet; Image; Compression

With the exploding demand in Internet applications and multimedia communications, image compression has become one of the most important areas in signal processing and communication. Well compressed images are close to the original images, but take much less space to store or are faster to transmit. Indeed, the network computing trend is moving towards high quality images for Web surfing and real- time video for video conferencing, and compression is necessary for their real-time transmission and reasonably small memory storage. Much engineering wisdom and heuristics have accumulated on image compression in recent years, and adaptivity (to the local characteristics of an image) is found to be the key to efficient image compression. This research follows an approach underlying many, if not all, important developments in statistics. That is, it aims at identifying problems from an applied field --(wavelet) image compression, developing their solutions in a statistical framework, and seeking answers and insights in this framework relevant to the original image compression problems. On one hand, this research uses and extends modern statistical estimation theory; on the other hand, it summarizes or formalizes engineering heuristics. Therefore it builds a bridge between image compression and statistical estimation literatures. The investigators are studying adaptive thresholding methods for wavelet image coding from the point of view of denoising and compression. They are also studying estimation and adaptive quantization methods based on quantized data for general digital signal compression including wavelet-based image compression. The statistical research establishes a framework for adaptive wavelet thresholding for images, and provides solutions to the general statistical problem of estimation based on quantized data, which are available in large quantities in the modern communications age. A level of effort statement. At the recommended level of support, the PI will make every attempt to meet the original scope and level of effort of the project.

随着互联网应用和多媒体通信需求的爆炸式增长，图像压缩已成为信号处理和通信中最重要的领域之一。压缩良好的图像接近原始图像，但存储空间更少或传输速度更快。事实上，网络计算的趋势正在朝着网上冲浪的高质量图像和视频会议的实时视频发展，而压缩对于它们的实时传输和相当小的内存存储是必要的。近年来，在图像压缩方面积累了很多工程智慧和启发，并且发现适应性（对图像的局部特征）是高效图像压缩的关键。这项研究遵循的方法是统计学中许多（如果不是全部）重要发展的基础。也就是说，它的目的是识别应用领域（小波）图像压缩中的问题，在统计框架中开发其解决方案，并在该框架中寻求与原始图像压缩问题相关的答案和见解。一方面，本研究运用并扩展了现代统计估计理论；另一方面，它总结或形式化了工程启发法。因此，它在图像压缩和统计估计文献之间架起了一座桥梁。 研究人员正在从去噪和压缩的角度研究小波图像编码的自适应阈值方法。他们还在研究基于量化数据的估计和自适应量化方法，用于通用数字信号压缩，包括基于小波的图像压缩。统计研究建立了图像自适应小波阈值的框架，并为基于现代通信时代大量可用的量化数据的估计的一般统计问题提供了解决方案。努力程度声明。 在建议的支持水平上，PI 将尽一切努力满足项目的原始范围和工作水平。