Image Compression Using Vector Quantization and Decision Trees

将研究用于图像压缩应用的可变速率树结构矢量量化器。重点将放在使用Breiman， Friedman， Olshen和Stone的树形设计技术的扩展和变化设计的树形结构代码上，但将考虑与树形结构代码相结合的各种其他矢量量化结构。特别是，有限状态和预测向量量化器将被考虑在编码中结合内存，二维子采样技术将被认为是增加有效向量大小、提高预测精度和提供自然数据结构的手段。将探讨在有限状态矢量量化器中对树进行分类和压缩的双重用途，以及在多光谱和彩色成像中对图像序列（如视频和常见物体或视图的多模态图像）进行压缩的组合。实验将使用医学图像、由视频、图像和图形混合组成的计算机图像数据以及卫星图像（特别是多光谱图像）进行。

Research will be conducted on variable rate tree-structured vector quantizers for image compression applications. The emphasis will be on tree-structured codes designed using extensions and variations of the tree design techniques of Breiman, Friedman, Olshen and Stone, but a variety of other vector quantization structures will be considered in combination with the tree- structured codes. In particular, both finite state and predictive vector quantizers will be considered for incorporating memory in the coding and two-dimensional subsampling techniques will be considered as a means of increasing effective vector size, improving prediction accuracy and providing a natural data structure. The dual use of trees for classification and compression in finite-state vector quantizers will be explored, as will be the combination of compression of image sequences such as video and multimodal images of a common object or view, as in multispectral and color imaging. Experiments will be conducted with medical images, computer image data consisting of mixed video, imagery, and graphics, and satellite imagery, especially multispectral.