Application of FPE based AR model for signal detection and digital data compression

1982 ◽  
Vol 34 (1) ◽  
pp. 181-188 ◽  
Author(s):  
F. Roy ◽  
Gurajada S. Murty
Keyword(s):  
Signal Detection ◽  
Data Compression ◽  
Digital Data ◽  
Ar Model

scholarly journals Storage Space Allocation Strategy for Digital Data with Message Importance

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 591
Author(s):  
Shanyun Liu ◽  
Rui She ◽  
Zheqi Zhu ◽  
Pingyi Fan
Keyword(s):  
Data Compression ◽  
Optimal Allocation ◽  
Reconstruction Error ◽  
Digital Data ◽  
Storage Space ◽  
Allocation Strategy ◽  
Storage Size ◽  
Space Allocation ◽  
Storage Space Allocation ◽  
Data Value

This paper mainly focuses on the problem of lossy compression storage based on the data value that represents the subjective assessment of users when the storage size is still not enough after the conventional lossless data compression. To this end, we transform this problem to an optimization, which pursues the least importance-weighted reconstruction error in data reconstruction within limited total storage size, where the importance is adopted to characterize the data value from the viewpoint of users. Based on it, this paper puts forward an optimal allocation strategy in the storage of digital data by the exponential distortion measurement, which can make rational use of all the storage space. In fact, the theoretical results show that it is a kind of restrictive water-filling. It also characterizes the trade-off between the relative weighted reconstruction error and the available storage size. Consequently, if a relatively small part of total data value is allowed to lose, this strategy will improve the performance of data compression. Furthermore, this paper also presents that both the users’ preferences and the special characteristics of data distribution can trigger the small-probability event scenarios where only a fraction of data can cover the vast majority of users’ interests. Whether it is for one of the reasons above, the data with highly clustered message importance is beneficial to compression storage. In contrast, from the perspective of optimal storage space allocation based on data value, the data with a uniform information distribution is incompressible, which is consistent with that in the information theory.

A NOVEL METHOD OF SOUND DATA DESCRIPTION BY MEANS OF CELLULAR AUTOMATA AND ITS APPLICATION TO DATA COMPRESSION

1999 ◽  
Vol 09 (06) ◽  
pp. 1211-1217 ◽  
Author(s):  
SHIGETOSHI NARA ◽  
NAOYA ABE ◽  
MASATO WADA ◽  
JOUSUKE KUROIWA
Keyword(s):  
Cellular Automata ◽  
Data Compression ◽  
Binary Data ◽  
Signal To Noise Ratio ◽  
Power Spectra ◽  
Digital Data ◽  
Data Description ◽  
Standard Data ◽  
Novel Method ◽  
Sound Data

A novel method of binary data description using cellular automata is proposed. As an actual example, several trials are made to describe vocal and musical sound data digitized in a standard data format. The reproduced sounds are evaluated by "actual listening", by calculating "the signal to noise ratio" with respect to the original sound data, and by comparing "the Fourier power spectra" with those of the original sounds. The results show that this method is quite effective and provides a new means of data compression applicable in any field of digital data recording or transferring.

scholarly journals Spilne bases in digital data compression problems

2000 ◽  
Vol 7 (3-4) ◽  
Author(s):  
А. Я. Білецький ◽  
I. B. Шелевицький ◽  
B. M. Шутко ◽  
Є. В. Ткаченко
Keyword(s):  
Data Compression ◽  
Digital Data
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