Machine Learning for Video Compression: Macroblock Mode Decision

Christoph Lampert

In: 18th International Conference on Pattern Recognition (ICPR 2006), Hongkong. International Conference on Pattern Recognition (ICPR) ICPR 2006.


Video Compression currently is dominated by engineer- ing and fine-tuned heuristic methods. In this paper, we pro- pose to instead apply the well-developed machinery of ma- chine learning in order to support the optimization of ex- isting video encoders and the creation of new ones. Exem- plarily, we show how by machine learning we can improve one encoding step that is crucial for the performance of all current video standards: macroblock mode decision. By formulating the problem in a Bayesian setup, we show that macroblock mode decision can be reduced to a classi- fication problem with a cost function for misclassification that is sample dependent. We demonstrate how to apply dif- ferent machine learning techniques to obtain suitable clas- sifiers and we show in detailed experiments that all of these perform better than the state-of-the-art heuristic method.

ChlMachineLearningForVideoComp.pdf (pdf, 128 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence