论文信息 - Speedup macroblock mode decision in H.264/SVC encoding using cost-sensitive learning

Speedup macroblock mode decision in H.264/SVC encoding using cost-sensitive learning

In this paper we present a novel macroblock mode decision algorithm to speedup H.264/SVC Intra frame encoding. We replace the complex mode-decision calculations by a classifier which has been trained specifically to minimize the reduction in RD performance. This results in a significant speedup in encoding. The results show that machine learning has a great potential and can reduce the complexity substantially with negligible impact on quality. The results show that the proposed method reduces encoding time to about 70% in base layer and up to 50% in enhancement layer of the reference implementation with a negligible loss in quality.

K. R. Ramakrishnan | Hari Kalva | Urvang Joshi | Chiranjib Bhattacharya | Rashad Jillani

[1] Heiko Schwarz,et al. Overview of the Scalable Video Coding Extension of the H.264/AVC Standard , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[2] Thomas Hofmann,et al. Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[3] Thorsten Joachims,et al. A support vector method for multivariate performance measures , 2005, ICML.

[4] Pedro Cuenca,et al. Very low complexity MPEG-2 to H.264 transcoding using machine learning , 2006, MM '06.

[5] Hari Kalva,et al. Using machine learning for fast intra MB coding in H.264 , 2007, Electronic Imaging.

[6] Hari Kalva,et al. Low complexity intra MB encoding in AVC/H.264 , 2009, IEEE Transactions on Consumer Electronics.