The pre-encoded MPEG-4 bitstreams for streaming video and interactive multimedia applications are needed to be converted to H.264/AVC bitstreams for the interoperability of heterogeneous video coding standards. A video transcoder is a device that converts video from a compressed format into another compressed format. This thesis proposes an efficient motion vector estimation method to reduce the computational complexity in MPEG-4 to H.264/AVC video transcoding and a cost estimation method to effectively determine the inter mode of H.264/AVC.
In order to effectively transcode a compressed bitstream from MPEG-4 to H.264/AVC, the transcoding method should exploit the decoded information of MPEG-4 bitstream and the characteristics of H.264/AVC. The proposed method estimates motion vectors for various block types of H.264/AVC by using the decoded of motion vectors of MPEG-4 for the spatial- and temporal-resolution reduction in the video transcoding. In order to compensate the estimated motion vectors, the motion vector refinement process is performed with the adaptive refinement range determined by the directional information of the estimated motion vectors.
The rate-distortion optimization (RDO) tool in H.264/AVC encoding chooses the best macroblock mode using Lagrangian optimization. Since the Lagrangian optimization needs to calculate the rate and the distortion for all candidate modes, the computational complexity is very high. This thesis proposes an efficient cost function for inter-mode decision using the Hadamard coefficients, which were already computed for sub-pixel motion estimation (ME).
Experimental results show our proposed method can achieve good R-D performance and quality-to-time ratio in MPEG-4 to H.264/AVC video transcoding.