Optimal normalisation of prediction residual for predictive coding with random access

Linear prediction serves as a mathematical operation to estimate the future values of a discrete-time signal based on a linear function of previous samples. When applied to predictive coding of waveform such as speech and audio, a common issue that plagues compression performance is the non-stationa...

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Bibliographic Details
Published in:IET signal processing Vol. 8; no. 7; pp. 710 - 719
Main Authors: Shu, Haiyan, Yu, Rongshan, Huang, Haibin
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 01.09.2014
John Wiley & Sons, Inc
Subjects:
adaptive codes
adaptive normalisation method
audio coding
Coders
Coding
Computing time
energy envelope
Entropy
entropy coder
entropy codes
Frames
future discrete‐time signal value estimation
linear prediction residuals
Mathematical analysis
mathematical operation
optimal prediction residual normalisation
poor coding performance mitigation
predictive waveform coding
Random access
random access frames
speech coding
subsequent entropy coder
Waveforms
adaptive normalisation method
random access frames
entropy codes
future discrete-time signal value estimation
entropy coder
adaptive codes
predictive waveform coding
audio coding
mathematical operation
subsequent entropy coder
speech coding
energy envelope
poor coding performance mitigation
optimal prediction residual normalisation
linear prediction residuals
ISSN:1751-9675, 1751-9683, 1751-9683
Online Access:Get full text
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Description
Summary:Linear prediction serves as a mathematical operation to estimate the future values of a discrete-time signal based on a linear function of previous samples. When applied to predictive coding of waveform such as speech and audio, a common issue that plagues compression performance is the non-stationary characteristics of prediction residuals around the starting point of the random access frames. This is because dependencies between prediction residuals and the historical waveform are interrupted to satisfy the random access requirement. In such cases, the dynamic range of the prediction residuals will fluctuate dramatically in such frames, leading to substantially poor coding performance in the subsequent entropy coder. In this study, the authors developed a solution to this long-standing issue by establishing a theoretical relationship between the energy envelope of linear prediction residuals in the random access frames and the prediction coefficients. Using the established relationship, an adaptive normalisation method is formulated as a preprocessor to the entropy coder to mitigate the poor coding performance in the random access frames. Simulation results confirm the superiority of the proposed method over existing solutions in terms of coding efficiency performance.
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ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2013.0403