Machine Learning for Video Compression: Macroblock Mode Decision

Christoph Lampert
18th International Conference on Pattern Recognition (ICPR 2006), Hongkong, ICPR, 2006
(submitted version)


Abstract:

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.

Files:

  ChlMachineLearningForVideoComp.pdf

BibTex:

@inproceedings{ LAMP2006,
	Title = {Machine Learning for Video Compression: Macroblock Mode Decision},
	Author = {Christoph Lampert},
	BookTitle = {18th International Conference on Pattern Recognition (ICPR 2006), Hongkong},
	Note = {(submitted version)},
	Year = {2006},
	Publisher = {ICPR}
}

     
Last modified:: 30.08.2016