Tilman Liebchen

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Lossless coding is to become the latest extension of the MPEG-4 audio standard. In response to a call for proposals, many companies have submitted lossless audio codecs for evaluation. The codec of the Technical University of Berlin was chosen as reference model for MPEG-4 audio lossless coding (ALS), attaining working draft status in July 2003. The encoder(More)
MPEG-4 Audio Lossless Coding (ALS) is a new extension of the MPEG-4 audio coding family. The ALS core codec is based on forward-adaptive linear prediction, which offers remarkable compression together with low complexity. Additional features include long-term prediction, multichannel coding, and compression of floating-point audio material. In this paper(More)
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(More)
Lossless coding will become the latest extension of the MPEG-4 audio standard. In response to a call for proposals, many companies have submitted lossless audio codecs for evaluation. The codec of the Technical University of Berlin was chosen as reference model for MPEG-4 Audio Lossless Coding, attaining working draft status in July 2003. The encoder is(More)
Three extension tools for extending and enhancing the compression performance of prediction-based lossless audio coding are proposed. The first extension aims at supporting floating-point data input in addition to integer PCM data. The second is progressive-order prediction of the starting samples at each random-access frame, where the information on(More)
MPEG-4 Audio Lossless Coding (ALS) is a new addition to the suite of MPEG-4 audio coding standards. The ALS codec is based on forward-adaptive linear prediction, which offers remarkable compression even with low predictor orders. Nevertheless, performance can be significantly improved by using higher predictor orders, more efficient quantization and(More)