Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps

In this work, we study the compression of multichannel signals with irregular sampling rates and data gaps. We consider state-of-the-art algorithms, which were originally designed to compress gapless signals with regular sampling, adapt them to operate with signals with irregular sampling rates and data gaps, and then evaluate their performance experimentally, through the compression of signals obtained from real-world datasets. Both the original algorithms and our schemes compress signals by exploiting their temporal, and, in some cases, spatial correlation. They work in a near-lossless fashion, guaranteeing a bounded absolute error between each decompressed sample and its original value. This includes the important lossless compression case, which corresponds to an error bound of zero. Our schemes first encode the position of the gaps, using arithmetic coding combined with a Krichevsky-Trofimov probability assignment on a Markov model, and then encode the data values separately. Our experimental analysis consists of comparing the compression performance of our schemes with each other, and with representative special-purpose and general-purpose lossless compression algorithms. We also measure and compare the schemes’ running times, to assess their practicality. From the results we extract some general conclusions: in the lossless case, TS2Diff and LZMA attain the best compression performance, whereas our adaptation of algorithm APCA is preferred for positive error bounds. At the same time, our adaptation of APCA, and TS2Diff, attain some of the best running times.