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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IET signal processing Ročník 8; číslo 7; s. 710 - 719
Hlavní autoři:	Shu, Haiyan, Yu, Rongshan, Huang, Haibin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Stevenage The Institution of Engineering and Technology 01.09.2014 John Wiley & Sons, Inc
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	1751-9675 1751-9683 1751-9683
DOI:	10.1049/iet-spr.2013.0403