Lossless data compression and decompression method based on lzw and lzw encoder and decoder

Abstract

The invention relates to an LZW-based lossless data compression and decompression method and an LZW encoder and decoder. The compression comprises: reading a character string to be compressed, decomposing the character string to be compressed into an original phrase sequence, and combining the original phrase sequence with a dictionary The data items in are encoded and matched, and the corresponding characters are output according to the matching results to obtain a compressed data stream; two non-negative integers are selected as the embedding parameters K and L, and the preset message is divided into consecutive groups of data blocks, each group of data blocks. It contains two sub-blocks, and each sub-block is represented by parameters k and l; the data block is embedded into the compressed data stream according to k and l to generate the final compressed data stream. For the LZW compressed data stream, the present invention selects part of the encoded codewords and dynamically adjusts the length of the corresponding compressed character string, and uses the dynamically adjusted codeword phrase pattern to embed extra message bits, without affecting the overall compression performance of the LZW algorithm. , because the coding rules and data formats are not changed, the compatibility and practicability are strong.

Description

technical field [0001] The invention belongs to the technical field of data compression, and in particular relates to an LZW-based lossless data compression and decompression method, an LZW encoder and a decoder. Background technique [0002] Under the guidance of the source-channel separation theory, in the current communication system, source coding realizes the problem of communication validity, and channel coding realizes the problem of communication reliability. Due to the limitations of the separation theorem, the design idea of ​​cascade coding makes the anti-interference ability of the coded sequence of the source very weak. In fact, the lack of repair ability of adaptive data compression is a prominent shortcoming in many applications. From the analysis of the source coding mechanism, it can be seen that the data obtained by source coding is almost without redundancy. After the data is transmitted through the channel, there are often bit errors due to the influence...

AI Technical Summary

Problems solved by technology

Although these methods can achieve certain results, the complexity of the algorithm is high, and the compression performance will be reduced. More importantly, due to the modification of the encoding rules and data format, it cannot be compatible with the standard algorithm, so the practicability is compromised.

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