A Triangulation-Based Backward Adaptive Motion Field Subsampling Scheme
Fabian Brand, Jürgen Seiler, Elena Alshina, André Kaup
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Optical flow procedures are used to generate dense motion fields which approximate true motion. Such fields contain a large amount of data and if we need to transmit such a field, the raw data usually exceeds the raw data of the two images it was computed from. In many scenarios, however, it is of interest to transmit a dense motion field efficiently. Most prominently this is the case in inter prediction for video coding.
In this paper we propose a transmission scheme based on subsampling the motion field. Since a field which was subsampled with a regularly spaced pattern usually yields suboptimal results, we propose an adaptive subsampling algorithm that preferably samples vectors at positions where changes in motion occur. The subsampling pattern is fully reconstructable without the need for signaling of position information. We show an average gain of 2.95 dB in mean squared error compared to regular subsampling. Furthermore we show that an additional prediction stage can improve the results by an additional 0.43 dB, gaining 3.38 dB in total.
In this paper we propose a transmission scheme based on subsampling the motion field. Since a field which was subsampled with a regularly spaced pattern usually yields suboptimal results, we propose an adaptive subsampling algorithm that preferably samples vectors at positions where changes in motion occur. The subsampling pattern is fully reconstructable without the need for signaling of position information. We show an average gain of 2.95 dB in mean squared error compared to regular subsampling. Furthermore we show that an additional prediction stage can improve the results by an additional 0.43 dB, gaining 3.38 dB in total.