Using machine learning for fast intra MB coding in H.264 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%.

Fast Intra size decision and mode decision algorithm for HEVC intra coding The latest High Efficiency Video Coding (HEVC) standard could achieve much higher coding efficiency than the current mainstream video coding standards (H.264AVC). Compared with H.264AVC, H.265 saves 50% cost than H.264AVC at the same quality. But compression ratio gets improved with at least 24 times increase in computational complexity. HEVC provides a quadtree based coding unit (CU) block partitioning structure. The predication mode of CU has been extended from 8 to 35. To alleviate the encoder computational complexity, we proposed an algorithm based on complexity of CU and cart decision tree to determine whether the current CU should be divided into quadtree. In the mode decision process, we reduce the number of candidate modes involved in the rough mode decision(RMD) and the rate distortion optimization (RDO) process. The experiment results demonstrate that the proposed algorithm could significantly reduce compression time with little loss.