RIA: Noiseless Compression of Grayscale Images

RIA：灰度图像的无噪声压缩

• 批准号: 9010361
• 负责人: Tenkasi Ramabadran
• 金额: $ 6万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-06-15 至 1992-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9010361&HistoricalAwards=false
• 关键词: RIA; Noiseless; Compression; Grayscale; Images

Noiseless (or reversible) methods are preferred over noisy (or irreversible) methods for the compression of grayscale images in situations where the quality of the images is critical and/or where postprocessing of the images is involved. Two significant application areas for such methods are in the storage and transmission of medical images and industrial radiographic images used for nondestructive evaluation (NDE). The main objective of the proposed research is to improve existing and develop new methods for the noiseless compression of grayscale images. Sophisticated source modeling techniques and the powerful arithmetic coding technique are used for this purpose. It is expected that some of the new methods to be developed will perform 30% to 40% better than the existing methods. Extensive statistical analyses of images and digital computer simulation experiments will be the primary tools in implementing the project. In addition to providing new, improved methods for the compression of grayscale images, the research project will also help shed some light on certain important aspects of the noiseless data compression area as a whole.

在图像质量至关重要和/或涉及图像后处理的情况下，对于灰度图像的压缩，无噪声（或可逆）方法优于有噪声（或不可逆）方法。这种方法的两个重要应用领域是用于无损评价（NDE）的医学图像和工业放射图像的存储和传输。提出的研究的主要目的是改进现有的灰度图像的无噪声压缩和开发新的方法。为此，使用了复杂的源建模技术和强大的算术编码技术。预计将开发的一些新方法的性能将比现有方法提高30%至40%。广泛的图像统计分析和数字计算机模拟实验将是实施该项目的主要工具。除了为灰度图像的压缩提供新的、改进的方法外，该研究项目还将有助于阐明整个无噪声数据压缩领域的某些重要方面。