Image Compression Using Vector Quantization and Decision Trees

研究变速率树型结构向量 用于图像压缩应用的量化器。 重点将 使用扩展设计的树形结构代码， Breiman，Friedman， Olshen和Stone，但各种其他矢量量化 结构将与树结合考虑- 结构化代码 特别是，有限状态和预测 矢量量化器将被考虑用于将存储器并入 编码和二维二次采样技术将 被认为是增加有效矢量大小的手段， 提高预测精度，提供自然数据 结构 树的双重用途是分类和 有限状态矢量量化器中的压缩将被探索， 将是图像序列的压缩的组合， 视频和多模式图像的共同对象或视图，如在 多光谱和彩色成像。 实验将在 对于医学图像，由混合视频组成的计算机图像数据， 图像、图表和卫星图像，特别是 多光谱

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.