LOSSLESS DATA COMPRESSION WITH LOW COMPLEXITY

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. A code length used in Golomb-Rice, which is typically referred to as the parameter k, is adapted for each sample in a predictable and repeatable manner to further reduce the size of a Golomb-Rice encoding for each sample. An infinite incident response filter of processed residuals automatically reduces influences of previously processed residuals upon such adaptation as additional samples are processed. In addition, the influence of each residual processed in the adaptation of the code length is directly related to the recency of the processing of the sample to which the residual corresponds. Furthermore, no resetting of the code length adaptation mechanism is required since influence of particularly distant residuals upon the adaptation of the code length diminishes to negligible amounts over time. The efficiency of Golomb-Rice encoding is improved by limiting the predicted samples to an efficient range. The maximum of the efficient range is the maximum valid value of a sample less the maximum positive value of the fixed-length, binary portion of an encoded residual. The adaptive predictor adapts to residuals between actual and predicted samples at a particular rate.