BP neural network based embedded system data compression/decompression method

Abstract

The invention discloses an embedded system data compression and decompression method based on BP neural network, which comprises the following steps: 1). Choice of the type of neural network; 2). The structure of the mapping relation; 3). Data compression of each standard string on a PC based on BP neural network; 4). Data decompression in the embedded system based on BP neural network; 5). To write the standard strings get from decompression in decompressed data file in turn; 6). To delete all special characters occurring at the end of the file. The invention has the advantages of simulating the mapping relation between line code and line data using neural network, meeting the purpose of data compression through using the information occupying less signal space to express the information occupying more signal space, breaking through the limit of traditionally only depending upon coding to lower data redundancy, realizing higher compression ratio, repeating multiple data compression to reach satisfactory compression ratio, effectively compressing compressed data with entropy coding and further improving compression effect.

Description

technical field [0001] The invention relates to the technical field of computer data processing, in particular to a method for realizing data compression and decompression in an embedded system. Background technique [0002] At present, the market demand for embedded system products continues to grow. However, because the embedded system is restricted in many aspects such as cost, volume, storage capacity, power consumption, speed, and processing ability, the huge amount of data has become a bottleneck in the development of the embedded system dictionary machine. If the data can be compressed to reduce the storage space of the data, the cost of the product can be reduced and the competitiveness of the product can be enhanced. Therefore, data compression technology has become a key technology in the development of embedded systems. [0003] Due to the limitations of the embedded system itself, such as the running speed is not high enough, the hardware resources are limited,...

AI Technical Summary

Problems solved by technology

Although many achievements have been made by traditional compression methods, it is difficult for traditional methods to make further progress in terms of compression ratio.

In addition, many original data still have a high degree of redundancy after traditional coding compression, and there is still a lot of compression "space", but it is difficult to further compress by similar methods

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