Adaptive context based sequential prediction for lossless audio compression

In this paper we propose the use of adaptive-context-based prediction in a sequential mode for lossless audio compression. We show that lossless compression algorithms with sequential context based prediction can achieve better compression results than with forward-frame-based linear prediction. Two distinct algorithms are proposed and evaluated for audio signal sampled at 48 kHz with 16 bits/sample. The context quantization and prediction in both algorithms are similar to those used in an algorithm previously proposed for image compression [7] but new solutions are provided for modelling of errors and collecting the coding statistics. The first algorithm uses histogram bucketing in a small number of contexts in conjunction with an arithmetic coder. The second algorithm uses parametric modelling of errors in a large number of contexts in conjunction with Golomb-Rice encoding.