A Compression Algorithm for Multi-streams Based on GEP
Chao Yi Ding, Changan Yuan, Xiao Qin, Yuzhong Peng · 2009
This paper applied the Methods which based on GEP in compress multi-streams. The contributions of this paper include: 1) giving an introduction to data function finding based on GEP(DFF-GEP), defining the main conception of Multi-Streams, and revealing the map relation in it; 2) putting forward the Compression Algorithm for Multi-Streams according to map relation lied in data between data streams; and 3)providing an experience with the real data and find that (3.1) the compression ratio of the new methods is 120~150 times as the traditional wavelets method, and 35~70 times as the wavelets and coincidence method; (3.2) the relative error of the new method is about 3%, yet maximum relative error is 0.01 by using the traditional relative error standard, the precision is improved from 7% to 15% as compared with the traditional method.