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O (log log n) Worst-Case Local Decoding and Update Efficiency for Data Compression

机译:O(log log n)最坏情况下的本地解码和数据压缩的更新效率

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This paper addresses the problem of data compression with local decoding and local update. A compression scheme has worst-case local decoding dwc if any bit of the raw file can be recovered by probing at most dwc bits of the compressed sequence, and has update efficiency of uwc if a single bit of the raw file can be updated by modifying at most uwc bits of the compressed sequence. This article provides an entropy-achieving compression scheme for memoryless sources that simultaneously achieves O (log log n) local decoding and update efficiency. Key to this achievability result is a novel succinct data structure for sparse sequences which allows efficient local decoding and local update.Under general assumptions on the local decoder and update algorithms, a converse result shows that the maximum of dwc and uwc must grow as Ω(log log n).
机译:本文解决了具有本地解码和本地更新的数据压缩问题。压缩方案具有最坏情况的本地解码d wc 如果最多可以通过探测d恢复原始文件的任何位 wc 压缩序列的位,并具有u的更新效率 wc 是否可以通过最多修改u来更新原始文件的单个位 wc 压缩序列的位。本文为无记忆源提供了一种实现熵的压缩方案,该方案同时实现了O(log log n)本地解码和更新效率。此可实现结果的关键是针对稀疏序列的新颖简洁数据结构,该结构可实现高效的本地解码和本地更新。在对本地解码器和更新算法的一般假设下,相反的结果表明d的最大值 wc 和你 wc 必须增长为Ω(log log n)。

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