Share Email Print

Proceedings Paper

Using machine learning for fast intra MB coding in H.264
Author(s): Hari Kalva; Lakis Christodoulou
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

H.264 is a highly efficient and complex video codec. The complexity of the codec makes it difficult to use all its features in resource constrained mobile devices. This paper presents a machine learning approach to reducing the complexity of Intra encoding in H.264. Determining the macro block coding mode requires substantial computational resources in H.264 video encoding. The goal of this work to reduce MB mode computation from a search operation, as is done in the encoders today, to a computation. We have developed a methodology based on machine learning that computes the MB coding mode instead of searching for the best match thus reducing the complexity of Intra 16x16 coding by 17 times and Intra 4x4 MB coding by 12.5 times. The proposed approach uses simple mean value metrics at the block level to characterize the coding complexity of a macro block. A generic J4.8 classifier is used to build the decision trees to quickly determine the mode. We present a methodology for Intra MB coding. The results show that intra MB mode can be determined with over 90% accuracy. The proposed can also be used for determining MB prediction modes with an accuracy varying between 70% and 80%.

Paper Details

Date Published: 29 January 2007
PDF: 4 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65082U (29 January 2007); doi: 10.1117/12.706024
Show Author Affiliations
Hari Kalva, Florida Atlantic Univ. (United States)
Lakis Christodoulou, Florida Atlantic Univ. (United States)

Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

© SPIE. Terms of Use
Back to Top