Low complexity intra MB encoding in AVC/H.264

Author

Jillani, Rashad ; Kalva, Hari

Author_Institution

Dept. of Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton, FL

Volume

55

Issue

1

fYear

2009

fDate

2/1/2009 12:00:00 AM

Firstpage

277

Lastpage

285

Abstract

In this paper we introduce and evaluate a novel machine learning based approach to reduce the complexity of Intra macroblock (MB) coding. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264/AVC video have a correlation with the intensities of adjacent MBs and sub-MBs. This paper also discusses and analyzes different approaches of using machine learning in Intra prediction. We discuss, amongst other features, slices, Intra prediction scheme for H.264 and data mining. We use data mining algorithms to develop decision trees for H.264 coding mode decisions. The proposed approach reduces the H.264/AVC MB mode computation process into a decision tree lookup with very low complexity. The proposed algorithm is implemented in reference software by modifying the source code and is compared with the JM reference software for H.264/AVC.

Keywords

data mining; decision trees; learning (artificial intelligence); video coding; AVC/H.264; Intra prediction; JM reference software; data mining; decision tree lookup; intra macroblock encoding; machine learning; video coding; Automatic voltage control; Data mining; Decision trees; Encoding; IEC standards; ISO standards; MPEG 4 Standard; Machine learning; Machine learning algorithms; Video coding; H.264/AVC; data mining; intra prediction; machine learning;

fLanguage

English

Journal_Title

Consumer Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0098-3063

Type

jour

DOI

10.1109/TCE.2009.4814446

Filename

4814446