Low Complexity H.264 Intra MB Coding

The H.264 video coding standard is highly efficient and at the same time highly complex. The complexity of encoding is substantially higher than prior standards such as H.263 and makes H.264 video encoding on mobile devices expensive. The few existing encoding solutions on mobile devices restrict the features used there by sacrificing quality for complexity. Fast encoding algorithms are necessary to fully exploit the advanced compression features offered by H.264. In this paper we present a novel machine learning based approach to reducing the complexity of intra MB coding. 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 1/3 of the reference implementation with a negligible loss in quality.

[1]  D. Marpe,et al.  The H . 264 / AVC Video Coding Standard , 2004 .

[2]  H. Kalva The H.264 Video Coding Standard , 2006, IEEE MultiMedia.

[3]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

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

[5]  Susanto Rahardja,et al.  Fast intra mode decision algorithm for H.264-AVC video coding , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[6]  Pedro Cuenca,et al.  Speeding-Up the Macroblock Partition Mode Decision in MPEG-2/H.264 Transcoding , 2006, 2006 International Conference on Image Processing.

[7]  C.-C. Jay Kuo,et al.  Feature-based intra-prediction mode decision for H.264 , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

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