Ultra-Low Rate Progressive Coding of Images

图像超低速率渐进编码

• 批准号: 9617366
• 负责人: Pamela Cosman
• 金额: $ 39.95万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 2001-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617366&HistoricalAwards=false
• 关键词: Ultra; Low; Rate; Progressive; Coding

Algorithms for progressive image compression seek to represent an image in such a way that a small fraction of the data file can allow a displayed image to be recognizable, and a larger portion of the data allows improved image quality. The main applications of progressive image compression are for browsing of remote databases, Internet image hopping, and fast remote decision-making tasks where many images must be scanned. The goal is to compress images to rates so low as to yield "just-recognizable" quality. The user can then exploit just- recognizable images while progressively scanning an image database by "quitting" each image as soon as it is determined not to be the one sought. This research involves theoretical understanding of ultra-low rate progressive image coding both with and without channel noise, as well as the development and evaluation of practical algorithms for applications such as low-bandwidth video compression and packet network image transmission. One portion of this research focuses on algorithm development, including optimization of wavelet zerotree coding for ultra-low rates, and coding using regions or line caricatures. The investigators are studying the effect of channel noise on progressive coding systems, and are developing both grayscale and color bit progressive image coding algorithms that do not exhibit catastrophic breakdown in the presence of channel noise. One promising approach uses rate compatible punctured convolution codes applied to images compressed with a wavelet zerotree method. Experimental studies of response times for human observers of progressive image coders will allow the researchers to quantitatively compare one progressive image coder against another, and yield insight into human perceptual responses to just-recognizable imagery.

用于渐进式图像压缩的算法寻求以这样的方式表示图像，即数据文件的一小部分可以允许所显示的图像是可识别的，并且数据的较大部分允许改进的图像质量。 渐进式图像压缩的主要应用是浏览远程数据库，Internet图像跳跃，以及必须扫描许多图像的快速远程决策任务。 其目标是将图像压缩到如此低的速率，以产生“刚刚可识别”的质量。 然后，用户可以利用仅可识别的图像，同时通过一旦确定每个图像不是所寻找的图像就“退出”每个图像来渐进地扫描图像数据库。 这项研究涉及到理论上的理解超低速率渐进图像编码与无信道噪声，以及实际的应用，如低带宽视频压缩和分组网络图像传输算法的开发和评估。 本研究的一部分集中在算法开发，包括优化小波零树编码的超低速率，和编码使用区域或线漫画。 研究人员正在研究信道噪声对渐进编码系统的影响，并正在开发灰度和彩色位渐进图像编码算法，这些算法在信道噪声存在时不会出现灾难性故障。 一种有前途的方法是使用速率兼容的穿孔卷积码应用于图像压缩小波零树方法。 对渐进式图像编码器的人类观察者的响应时间的实验研究将使研究人员能够定量地比较一个渐进式图像编码器与另一个渐进式图像编码器，并深入了解人类对可识别图像的感知反应。