Lossless data compression with low complexity

Abstract

An adaptive linear predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding. Linear prediction of samples of a signal which represents digitized sound tends to produce relatively low residuals and those residuals tend to be distributed exponentially. Accordingly, linear prediction combined with Golomb-Rice encoding produces particularly good compression rates with very efficient and simple implementation. The accuracy of the linear predictor is improved by including, in the prediction of a current sample of a first channel of the digitized signal, look-ahead sample data from a corresponding second channel of the digitized signal. For example, prediction of a right channel sample of a digitized, stereo, audio signal is improved by inclusion of look-ahead left channel sample data in the right channel sample predictor. Since correlated channels of a digitized signal have a relatively high degree of correlation, accuracy of sample prediction is improved and residuals between the predicted samples and actual samples are reduced, thereby reducing the amount of data required to represent the residuals. In reconstructing the encoded samples, a decoder uses a current reconstructed sample of the first channel of a digitized signal to predict a current sample of the second corresponding channel.