Burst Error Recovery Method for LZSS Coding

Abstract

SUMMARYSince 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.

On unequal error protection for LZSS compressed data

“…In such cases, unequal error protection schemes (UEP) [17][18][19][20][21] are considerably more efficient, in terms of final compression efficiency, than equal error protection schemes (EEP). The methods in [20,21] were specifically proposed for compressed data using Lempel-Ziv-Storer-Szymanski (LZSS) [6], a variant of LZ77 [5]. LZSS is widely utilized in text applications, due to its great efficiency for compressing that kind of data [1,6].…”

“…The new solution is compared to two methods found in the literature [20,21]. The method in [21] presents a very high efficiency while requiring a very long buffer at the receiver.…”

“…Therefore, its utilization in real-time applications is prohibitive. By its turn, the method in [20] works with a very small buffer when compared with the method in [21], but at the expense of a considerably reduced compression efficiency. Through a careful design, the scheme proposed in this paper is able to work with very short buffers as the method in [20].…”

“…By its turn, the method in [20] works with a very small buffer when compared with the method in [21], but at the expense of a considerably reduced compression efficiency. Through a careful design, the scheme proposed in this paper is able to work with very short buffers as the method in [20]. Moreover, computer simulations considering the Calgary Corpus database (http://www.datacompression.info/Corpora/CalgaryCorpus/), which is commonly utilized for testing compression algorithms [22], showed that the proposed scheme achieves a compression efficiency closer to that of the method in [21].…”

“…These methods aim to maintain the same efficiency of the original compression scheme while adding some error resilience. There are some important differences between the approach in [27,28] and that used in [20,21]. The former keeps the same compression efficiency as the original Lempel-Ziv methods, while adding some error resilience, whose amount cannot be predicted since it depends on the data to be compressed.…”