Lossless audio coding using code-excited linear prediction with embedded entropy coding

Abstract

Audio coding algorithm is used to compress and decompress digital audio data. Generally, there are two classes of audio coding techniques; lossy coding and lossless coding. Lossy coders such as MP3, can achieve high compression ratio, however the reconstructed signal is not exactly the same as the original signal. Lossless coders can reconstruct a signal identical to the original signal but the compression performance is not as good as that of lossy coders. With increasing demand of high-fidelity audio, most efforts in audio coding research are directed to lossless compression of high quality audio. Recent research trends in lossless audio compression focus on a decorrelation-plus-entropy coding approach. The main disadvantage of this approach is that it is not scalable to cater for channels with different bandwidth. In addition, the order of the decorrelation filter is generally very high and to compute the filter coefficients on the fly in real time is quite a demanding task both in the encoder and decoder. In this thesis, a scalable coding algorithm for lossless compression of audio signals is presented. The proposed algorithm consists of a lossy coding part and a lossless coding part. The lossy coding part is based on code excitation approach which is similar to Code-Excited Linear Prediction (CELP) coding for coding speech signal. However, the proposed approach has fundamental differences from CELP coding in many aspects, in particular, the excitation gain and short term prediction coefficients are adapted in a sample-by-sample fashion to cope with rapid time-varying nature of audio signals. In addition, a block adaptive linear predictive filter is added in the proposed coder to further improve short term prediction. The excitation codebook is a fixed codebook constructed from a collection of stochastic codewords which are obtained by training from a large collection of real audio signals. A total of eleven stochastic codebooks for various bit rates were designed. During encoding the excitation codebook is searched by using an M-L tree search strategy with a joint optimization based on minimum error energy and minimum code length after entropy coding. Several codebook selection methods and M-L tree search strategies are discussed and compared in order to determine the best compression performance at various encoding complexities. In the lossless coding part, the error between the input and the synthetic signal has significantly lower entropy which can then be encoded by an arithmetic coder to achieve lossless compression. If the residual information is not sent to the decoder, the decoder can recover a reasonable good quality of audio signal from the received lossy coding parameters only. The proposed coding algorithm has very low decoding complexity due to its simple code excitation structure and achieves compression performance comparable to other advanced lossless coders for coding CD quality audio.

Date of Award	15 Jul 2011
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Cheung Fat CHAN (Supervisor)