Method and device for hybrid coding/decoding of multi-channel lossless audios

Abstract

The invention discloses a method and a device for hybrid coding/decoding of multi-channel lossless audios. In the method and the device, the coding/encoding of an algorithm and a Golomb-rice entropy is chosen according to sampling frequency, quantity of the channels, digitalization bits and computing complexity of a pre-treatment module and a predication module; and the hybrid coding/decoding of the algorithm and the Golomb-rice for multi-channel lossless audio signals is realized by the respective coding/decoding advantages of the algorithm and the Golomb-rice entropy. For the consideration of the balance between the entropy coding complexity and the coding/decoding efficiency, a hybrid entropy coding method applicable to the multi-channel lossless audio data is provided; the switching between the coding/decoding complexity and the coding/decoding speed can be realized by using the method; and wider application requirements can be satisfied according to the difference of the operational capabilities of processors of a coders and an encoder. The high-quality coding for the multi-channel lossless audios can be realized and the hardware cost is reduced effectively.

Description

technical field [0001] The invention relates to digital signal processing technology, in particular to a digital audio source codec method and device. Background technique [0002] With the development of semiconductor technology, broadband network technology and large-capacity storage technology, people's demand for multi-channel, high-sampling frequency lossless audio signals is becoming stronger and stronger. However, due to the increase in the number of channels and the increase in the frequency of use, it also brings about a doubling of the computational complexity of the lossless audio codec. The main factors affecting the computational complexity of encoding and decoding are: the number of channels, the frequency used, the number of quantization bits, the complexity of the encoding and decoding algorithm, etc.; the number of channels, the frequency used, the number of quantization bits and other factors are objective input conditions, and the encoding and decoding alg...

AI Technical Summary

Problems solved by technology

These entropy coding algorithms are suitable for the compression of data sources with different statistical characteristics, and the time and space complexities are also quite different.

Huffman coding is difficult to separate probability model modeling and coding. In terms of compression rate, extended Huffman coding can approach the entropy of the source, but its space complexity and time complexity are relatively large; compared with Huffman Coding ratio, arithmetic coding is easier to separate probability model modeling from coding. It is the coding of the entire information source, and its compression rate can also approach the entropy of the information source, but the refresh of the probability table is time-consuming and time-complexity is relatively large ; Golomb-rice compresses data based on the probability model of geometric distribution, the compression rate is slightly worse, but its time and space complexity is low; and run-length coding is only suitable for continuous and repeated data compression, which lacks versatility

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