Collaborative: Compressed Domain Search for Text and Images by Sorted Contexts

随着多媒体数据量的急剧增加，效率方面的考虑使得考虑尽可能多地将数据保持在压缩形式的方法变得重要，即使在搜索时也是如此。我们建议开发的方法直接搜索压缩的文本和图像，当压缩是基于家庭的压缩算法，依赖于排序的上下文。我们还提出了基于排序上下文的文本和图像搜索感知数据压缩方案，该方案将支持直接在压缩数据上进行压缩域搜索。对于文本，我们将探索经典的线性精确模式匹配算法和二分搜索算法在Burrows-Wheeler变换文本压缩中的适用性。我们将开发类似的压缩方案的图像排序的图像上下文的基础上，calledLICSIC（无损图像压缩排序的图像上下文）。所提出的研究结果将是重要的环境中，需要组织和管理的textor图像的巨大集合。因特网搜索引擎、数字图书馆、卫星和空间数据、地理信息系统、生物信息学和医疗诊断是典型的候选应用。

With the dramatic increase in the amount of multimedia data, efficiencyconsiderations make it important to consider ways to keep the data in the compressed form for as much as possible, even when it is searched. We propose to develop methodologies for searching directly on compressed text and images, when the compression is based on the family of compression algorithms that depend on sorted context. We also propose to develop search-aware data compression schemes for text and images based on sorted context that will support compressed-domain search directly on the compressed data.For text, we will explore the applicability of the classical linear exact pattern matching algorithms and binary search algorithms for textcompressed with the Burrows-Wheeler transform. We will develop similarcompression schemes for images based on sorted image contexts, calledLICSIC (Lossless Image Compression by Sorted Image Contexts).The results of the proposed research will be important in environmentsthat require the organization and management of huge collections of textor images. Internet search engines, digital libraries, satellite and space data , geographical information systems, bio-informatics and medical diagnostics are typical candidate applications.