A Channel Differential EZW Coding Scheme for EEG Data Compression

2011 ◽  
Vol 15 (6) ◽  
pp. 831-838 ◽  
Author(s):  
V. R. Dehkordi ◽  
H. Daou ◽  
F. Labeau
Keyword(s):  
Data Compression ◽  
Coding Scheme ◽  
Eeg Data

EEG data compression techniques

1997 ◽  
Vol 44 (2) ◽  
pp. 105-114 ◽  
Author(s):  
G. Antoniol ◽  
P. Tonella
Keyword(s):  
Data Compression ◽  
Eeg Data

scholarly journals Fast DCT algorithms for EEG data compression in embedded systems

2015 ◽  
Vol 12 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Darius Birvinskas ◽  
Vacius Jusas ◽  
Ignas Martisius ◽  
Robertas Damasevicius
Keyword(s):  
Embedded Systems ◽  
Data Compression ◽  
High Speed ◽  
Communication Channel ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Transform Coefficients

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.

scholarly journals Distributed video coding scheme of multimedia data compression algorithm for wireless sensor networks

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Ning Ma
Keyword(s):  
Sensor Networks ◽  
Video Coding ◽  
Data Compression ◽  
Distributed Video Coding ◽  
Video Codec ◽  
Multimedia Data ◽  
High Definition ◽  
Process Data ◽  
Coding Scheme ◽  
Network Transmission

AbstractThe emergence of multimedia data has enriched people’s lives and work and has penetrated into education, finance, medical, military, communications, and other industries. The text data takes up a small space, and the network transmission speed is fast. However, due to its richness, the multimedia data makes it occupy an ample space. Some high-definition multimedia information even reaches the GB level, and the multimedia data network transmission is relatively slow. Compared with the traditional scalar data, the multimedia data better describes the characteristics of the transaction, but at the same time, the multimedia data itself has a large capacity and must be compressed. Nodes of wireless multimedia sensor networks have limited ability to process data. Traditional data compression schemes require high processing power of nodes and are not suitable for sensor networks. Therefore, distributed video codec scheme in recent years becomes one of the hot multimedia sensor network technologies, which is a simple coding scheme, coding complexity of decoding performance. In this paper, distributed video codec and its associated knowledge based on the study present a distributed video coding scheme and its improvements. Aiming at the problem that the traditional distributed video coding scheme cannot accurately decode the motion severe region and the boundary region, a distributed video coding algorithm based on gradient-domain ROI is proposed, which can enhance the coding efficiency of the severe motion region and improve the decoded image while reducing the code rate and quality, ultimately reducing sensor node energy consumption.

scholarly journals Seismic Data Compression Using 2D Lifting-Wavelet Algorithms

2015 ◽  
Vol 11 (21) ◽  
pp. 221-238 ◽  
Author(s):  
Carlos Fajardo ◽  
Oscar Mauricio Reyes ◽  
Ana Ramirez
Keyword(s):  
Wavelet Transform ◽  
Data Compression ◽  
Seismic Data ◽  
Data Sets ◽  
Coding Scheme ◽  
Filter Type ◽  
Three Stages ◽  
Seismic Data Compression ◽  
And Storage ◽  
Transforma Tion

Different seismic data compression algorithms have been developed in or-der to make the storage more efficient, and to reduce both the transmission time and cost. In general, those algorithms have three stages: transforma-tion, quantization and coding. The Wavelet transform is highly used tocompress seismic data, due to the capabilities of the Wavelets on representing geophysical events in seismic data. We selected the lifting scheme to implement the Wavelet transform because it reduces both computational and storage resources. This work aims to determine how the transforma-tion and the coding stages affect the data compression ratio.Several 2Dlifting-based algorithms were implemented to compress three different seis-mic data sets. Experimental results obtained for different filter type, filterlength, number of decomposition levels and coding scheme, are presented in this work.

Universal Data Compression Algorithm Based on Approximate String Matching

1996 ◽  
Vol 10 (4) ◽  
pp. 465-486 ◽  
Author(s):  
Ilan Sadeh
Keyword(s):  
Data Compression ◽  
Stationary Process ◽  
Source Coding ◽  
Empirical Distribution ◽  
String Matching ◽  
Deterministic Algorithm ◽  
Approximate String Matching ◽  
Output Process ◽  
Infinite Time ◽  
Coding Scheme

A practical source coding scheme based on approximate string matching is proposed. It is an approximate fixed-length string matching data compression combined with a block-coder based on the empirical distribution. A lemma on approximate string matching, which is an extension of the Kac Lemma, is proved. It is shown, based on the lemma, that the deterministic algorithm converts the stationary and ergodic source, u, into an output process v, and under the assumption that v is a stationary process, after the scheme has run for an infinite time, the optimal compression ratio R(D) is achieved. This reduces the problem of the universal lossy coder to the proof of stationarity of the output process ν in the proposed algorithm. The main advantages of the proposed method are the asymptotic sequential behavior of the encoder and the simplicity of the decoder.

Sign in / Sign up

Export Citation Format

Share Document