Adaptative signal sampling and sample quantization for resource-constrained stream processing

Abstract

We propose a low-complexity encoding strategy for efficient compression of biomedical signals. At the heart of our approach is the combination of non-uniform signal sampling together with sample quantization to improve the source coding efficiency. We propose to jointly extract and quantize information (data samples) most relevant to the application processing the incoming data in the backend unit. The proposed joint sampling and quantization method maximizes a user-defined utility metric under system resource constraints such as maximum transmission rate or encoding computational complexity. We illustrate this optimization problem on electrocardiogram (ECG) signals, using the Percentage Root-mean-square Difference (PRD) metric as the utility function measuring the distortion between the original signal and its reconstructed (inverse quantization and linear interpolation) version. Experiments conducted on the MIT-BIH ECG corpus using the well-accepted FAN algorithm as the non-uniform sampling method show the effectiveness of our joint strategy: Same PRD as 'FAN alone' at half the data rate for less than three times the (low) computational complexity of FAN alone.