Lossless audio coding based on high order context modeling
Tong Qiu
Abstract
Tong Qiu
Abstract
Lossless audio coding has become a significant research topic. Most of coding methods are based on linear prediction and Huffman coding or Rice coding is used for entropy coding. We present a new lossless audio coding where the high order context modeling is used for the entropy coding. The linear prediction is first applied to the original audio signal with prediction error feedback. The prediction errors are entropy coded using conditional probabilities so that the coding performance can be improved.
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Lossless audio coding has become a significant research topic. Most of coding methods are based on linear prediction and Huffman coding or Rice coding is used for entropy coding. We present a new lossless audio coding where the high order context modeling is used for the entropy coding. The linear prediction is first applied to the original audio signal with prediction error feedback. The prediction errors are entropy coded using conditional probabilities so that the coding performance can be improved.
Key concepts: Tunstall coding, Huffman coding, Entropy encoding, Variable-length code, Shannon–Fano coding, Context-adaptive variable-length coding, Computer science, Context-adaptive binary arithmetic coding