New Intra Prediction with Finite State Machine for H.264AVC This paper proposes a new approach to improve the coding performance of intra block coding in H.264AVC via finite state machine. Grounding on high correlation between neighboring blocks, finite state machine is employed both at encoder and decoder to reduce the number of bits required for encoding to enhance coding performance. Two extra intra prediction modes are created in our proposed method. Through these two modes, the number of bits required to denote the current block is greatly reduced and low bit rate can be achieved. Experimental results show that the proposed method can greatly improve coding efficiency of intra macroblock coding in H.264AVC.

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%.