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Adaptive context-based sequential prediction for lossless audio compression
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DOI：10.1016/S0165-1684(00)00117-1.png)
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Abstract
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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. The proposed coding scheme uses parametric modelling of errors in a large number of contexts in conjunction with Golomb-Rice encoding. Context quantization and prediction are similar to those introduced in an algorithm previously proposed for image compression (Tabus et al., Proceedings of ICIP'97 International Conference on Image Processing, Santa Barbara, California, October 1997, pp. 401-404) but new solutions are provided to cope with the wide dynamical range of the prediction error and, optionally, to exploit the long-time dependencies, very common in audio or speech signals. The experimental results show the good performance of the proposed technique for audio signals sampled at 48 kHz with 16 bits/sample while the variant including long-time prediction is shown to perform very well for speech signal sampled at 8 kHz with 16 bits/sample. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
lossless compression
adaptive prediction
context algorithm
audio compression
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