NDLI: A low-cost MAD prediction algorithm for H.264 rate control facilitating hardware implementation

Content Provider	IEEE Xplore Digital Library
Author	Jia Wang HaiBing Yin BingQian Zhou Ning Xu
Copyright Year	2011
Description	Author affiliation: Department of Information Engineering, China Jiliang University, Hangzhou, China (Jia Wang; HaiBing Yin; BingQian Zhou; Ning Xu)
Abstract	This paper presents a low complexity macroblock (MB) level H.264/AVC rate control (RC) algorithm from system-level design viewpoint for video encoder hardware implantation. MB pipeline structure and data reuse efficiency are simultaneously take into consideration. In order to improve the data processing efficiency, hardware encoder usually adopts zigzag scanning MB encoding sequence order instead of raster one. The proposed low-cost RC algorithm resolves the problem of data dependency without increasing processing latency. Moreover, the proposed algorithm adopts a new MB level mean absolute difference (MAD) prediction method to improve the MAD prediction efficiency in G012 proposal. Linear prediction method in G012 resulting in lower complexity and memory requirements not only reduce the complexity of RC algorithm but also the memory buffer. Simulation results show only 32%, 68%, and 88% hardware cycles are desired for QCIF, CIF and D1 format video real-time coding respectively in the proposed RC algorithm compared with G012, while maintaining the same PSNR compared with G012.
Starting Page	18
Ending Page	21
File Size	282133
Page Count	4
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6099962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis MAD MB pipeline Pipelines Computer architecture Prediction methods Prediction algorithms Hardware Encoding rate control H.264/AVC zigzag
Content Type	Text
Resource Type	Article

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