An adaptive non-linear motion vector resampling algorithm for down-scaling video transcoding

In this paper, we describe an adaptive non-linear motion vector resampling algorithm for video transcoding where spatial resolution down-scaling is involved. The incoming MPEG-2 video is spatially down-scaled by a factor of two, and transcoded to MPEG-1 video at lower bit rate. Since the heaviest computational load of encoding is the motion estimation process, motion vectors of the original video have to be reused from the bit stream. However, reusing motion vectors with down-scaling is not easy, since the four motion vectors to be down-scaled from original video are usually not well aligned. The motion vector reusing scheme greatly affects the performance of the transcoder since the decoded video quality depends on the performance of motion compensation as well as rate control. The performance of an alternative weighted averaging scheme tends to be degraded by inappropriate motion vectors. Experiments show that a motion vector selection algorithm indeed performs better than a weighted average method. The selection algorithm uses a likelihood score based on the investigation of statistical characteristics of the macroblocks associated with the best matching motion vectors. The motion vector having the highest likelihood score is selected to be reused. The weighted averaging scheme is also applied if there are macroblocks having the same highest scores. The experimental results with various bit rate and sequences show that the proposed algorithm provides improvement of PSNR by 0.2/spl I.utilde/0.7 dB on average and that the computational complexity is not much higher. Therefore, the overall transcoding performance and efficiency can be increased by the proposed algorithm.