Lossless coding will become the latest extension of the MPEG-4 audio standard. The lossless audio codec of the Technical University of Berlin was chosen as reference model for MPEG-4 Audio Lossless Coding (ALS). The MPEG-4 ALS encoder is based on linear prediction, which enables high compression even with moderate complexity, while the corresponding decoder is straightforward. The paper describes the basic elements of the codec as well as some additional features, gives compression results, and points out envisaged applications.
Two extension tools for enhancing the compression performance of prediction-based lossless audio coding are proposed. One is progressive-order prediction of the starting samples at the random access points, where the information of previous samples is not available. The first sample is coded as is, the second is predicted by first-order prediction, the third is predicted by second-order prediction, and so on. This can be efficiently carried out with PAR-COR (PARtial autoCORrelation) coefficients. The second tool is interchannel joint coding. Both predictive coefficients and prediction error signals are efficiently coded by interchannel differential or three-tap adaptive prediction. These new prediction tools lead to a steady reduction in bit rate when random access is activated and the interchannel correlation is strong.
This paper provides a brief overview of an emerging ISO/IEC standard for lossless audio coding, MPEG-4 ALS and explains the choice of algorithms used in its design, and compare it to current state-of-the-art algorithms for lossless audio compression.
The MPEG-4 Audio Lossless Coding (ALS) standard belongs to the family MPEG-4 audio coding standards. In contrast to lossy codecs such as AAC, which merely strive to preserve the subjective audio quality, lossless coding preserves every single bit of the original audio data. The ALS core codec is based on forward-adaptive linear prediction, which combines remarkable compression with low complexity. Additional features include long-term prediction, multichannel coding, and compression of floating-point audio material. This paper describes the basic elements of the ALS codec with a focus on prediction, entropy coding, and related tools and points out the most important applications of this standardized lossless audio format.
We propose an efficient lossless coding algorithm that not only handles both PCM format data and IEEE floating-point format data, but also provides end users with a random access property. In the worst-case scenario, where the proposed algorithm was applied to artificially generated full 32 bit floating-point sound files with 48 kHz or 96 kHz sampling frequencies, an average compression rate of more than 1.5 and 1.7, respectively, was still achieved, which is much better than the average compression rate of less than 1.1 achieved by the general purpose lossless coding algorithm, gzip. Moreover, input sound files with samples' magnitudes out-of-range can also be perfectly reconstructed by our algorithm.
Recent papers have proposed linear prediction as a useful method for lossless audio coding. Transform coding, however, hasn’t been investigated so far, although it seems to be more adapted to the harmonic structure of most audio signals. In this paper we present first results on lossless transform coding of CD-quality audio data. One main aspect lies on a suitable quantization method to obtain perfect reconstruction. Using a codebook with different entropy codes for the transform coefficients we achieve bitrates, slightly better then those obtained by the lossless linear prediction schemes mentioned above.
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