Improved Motion Classification Techniques for Adaptive Multi-Pattern Fast Block-Matching Algorithm

In several video coding standards, such as H.264, motion estimation becomes the most-time consuming subsystem. Therefore, recently research on video coding focuses on the development of novel algorithms able to save computations with minimal effects over the coding distortion. Due to the fact that real video sequences usually exhibit a wide-range of motion content, from uniform to random, and to the vast amount of coding applications demanding different degrees of coding quality, adaptive algorithms have revealed as the most robust general purpose solutions. In particular, multi-pattern algorithms can adapt to video contents as well as to required coding quality by means of the use of a set of heterogeneus search patterns, each one adapting better to particular motion and quality requirements. This paper applies some improvements to the Motion Classification based Search, an adaptive multi-pattern algorithm based on motion classification techniques. Our experimental results show that MCS notably reduces the computational cost with respect to some well-known algorithms while maintaining the quality.