Comparison of prediction schemes with motion information reuse for low complexity spatial scalability

摘要

Three low complexity algorithms that allow spatial scalability in the context of video coding are presented in this paper. We discussed the feasibility of reusing motion and residual texture information of the base layer in the enhancement layer. The prediction errors that arise from the discussed filters and schemes are evaluated in terms of the Mean of Absolute Differences. For the interpolation of the decoded pictures from the base layer, the presented 6-tap and bicubic filters perform significantly better than the bilinear and nearest neighbor filters. In contrast, when reusing the motion vector field and the error pictures of the base layer, the bilinear filter performs best for the interpolation of residual texture information. In general, reusing the motion vector field and the error pictures of the base layer gives the lowest prediction errors. However, our tests showed that for some sequences that have regions with complex motion activity, interpolating the decoded picture of the base layer gives best result. This means that an encoder should compare all possible prediction schemes combined with all interpolation filters in order to achieve optimal prediction. Obviously this would not be possible for real-time content creation.