Diversity and importance measures for video downscaling

Abstract

In video downscaling, simply reusing the motion vectors extracted from an incoming video bitstream may not result in good quality pictures. Several refinement schemes have been proposed recently to correct their recomposed new motion vectors in order to optimize the coding efficiency during the transcoding process. However, the major concern is that an optimal resampled motion vector may not exist during video downscaling process. In other words, it is difficult to use one motion vector to represent four motion vectors when the diversity of the incoming motion vectors is high. Besides, redundant computation for refinement has also been carried out even if the resampled motion vector is already optimal. Motivated by this, we propose an adaptive motion vector re-composition algorithm using two new measures: the diversity and importance measures of motion vectors. Using the importance measure, our proposed scheme manages to differentiate the most representative motion vector as a consideration to recompose a new motion vector. In addition, the diversity measure provides information for the video transcoder controlling the size of the refinement window to achieve a significant reduction of computational complexity. Experimental results show that our proposed adaptive motion vector re-composition scheme provides a high coding efficiency in terms of both quality and complexity.