Burst Error Recovery Method for LZSS Coding
スポンサーリンク
概要
- 論文の詳細を見る
Since the compressed data, which are frequently used in computer systems and communication systems, are very sensitive to errors, several error recovery methods for data compression have been proposed. Error recovery method for LZ77 coding, one of the most popular universal data compression methods, has been proposed. This cannot be applied to LZSS coding, a variation of LZ77 coding, because its compressed data consist of variable-length codewords. This paper proposes a burst error recovery method for LZSS coding. The error sensitive part of the compressed data are encoded by unary coding and moved to the beginning of the compressed data. After these data, a synchronization sequence is inserted. By searching the synchronization sequence, errors in the error sensitive part are detected. The errors are recovered by using a copy of the part. Computer simulation says that the compression ratio of the proposed method is almost equal to that of LZ77 coding and that it has very high error recovery capability.
- (社)電子情報通信学会の論文
- 2009-12-01
著者
-
Kitakami Masato
Graduate School Of Advanced Integration Science Chiba University
-
KAWASAKI Teruki
Faculty of Engineering, Chiba University
-
Kawasaki Teruki
Faculty Of Engineering Chiba University
-
Kitakami Masato
Graduate School of Advanced Integrarion Science, Chiba University
関連論文
- Grid Monitoring System based on GMA
- A Class of Error Locating Codes : SEC - S_EL Codes
- Trust Management of Grid System Embedded with Resource Management System
- Dependability Improvement for PPM Compressed Data by Using Compression Pattern Matching
- Burst Error Recovery Method for LZSS Coding
- A Checkpointing Method with Small Checkpoint Latency
- Integrating trust into scheduling algorithms in Grid system (ディペンダブルコンピューティング)
- Proposal of Grid Monitoring System with Fault Tolerance
- Proposal of Grid Monitoring System with Fault Tolerance
- Neighborhood Level Error Control Codes for Multi-Level Cell Flash Memories
- Mobile device prediction for location-based cloud service