Tree-structured Image Compression and Classification

格雷树结构矢量量化是一种图像压缩方法，它应用统计聚类算法、树形结构分类和回归算法的思想来生成以近乎最佳的方式折衷比特率和平均失真的压缩代码。这项研究正在研究将这两种形式的信号处理--压缩和分类--明确地组合成单一的树形结构算法，该算法允许在传统的失真度量(如平方误差)和分类精度度量(如贝叶斯风险)之间进行权衡。其目的是产生具有隐含分类信息的代码，即，存储或传送的压缩图像包含分类信息，而无需进一步的信号处理。这样的系统可以提供直接的低级分类，或者为更复杂的全帧识别算法提供有效的前端。针对相对较大块大小的矢量量化算法也在开发中，重点是多分辨率压缩算法。为了改善在初步研究或组合压缩和分类中发现的有希望的性能，将有必要使用更大的块大小，或者等效地，更多的上下文。多分辨率或分层量化器提供了一种简单而有效的方法来实现这一点。正在探索其他相关问题，包括用于预测矢量量化和图像序列编码的改进预测方法。

Gray Tree-structured vector quantization is an approach to image compression that applies ideas from statistical clustering algorithms and tree-structured classification and regression algorithms to produce compression codes that trade off bit rate and average distortion in a near optimal fashion. This research is examining the explicit combination of these two forms of signal processing, compression and classification, into single tree-structured algorithms that permit a trade off between traditional distortion measures, such as squared error, with measures of classification accuracy such as Bayes risk. The intent is to produce codes with implicit classification information, that is, for which the stored or communicated compressed image incorporates classification information without further signal processing. Such systems can provide direct low level classification or provide an efficient front end to more sophisticated full-frame recognition algorithms. Vector quanitization algorithms for relatively large block sizes are also being developed with an emphasis on multiresolution compression algorithms. In order to improve the promising performance found in preliminary studies or combined compression and classification, it will be necessary to use larger block sizes or, equivalently, more context. Multiresolution or hierarchial quantizers provide a simple and effective means of accomplishing this. Other related issues are being explored, including improved prediction methods for predictive vector quantization and image sequence coding.