Vector quantization and signal compression
Vector quantization and signal compression
Context-based lossless and near-lossless compression of EEG signals
IEEE Transactions on Information Technology in Biomedicine
Lossless compression of continuous-tone images via context selection, quantization, and modeling
IEEE Transactions on Image Processing
International Journal of Telemedicine and Applications
International Journal of Telemedicine and Applications
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Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.