Adaptive Boundary Extension for Inter Prediction
Nicolas Horst, Priyanka Das, Mathias Wien · 2021
Boundary extension refers to the extension of a picture boundary to enable inter prediction from regions outside the picture. In current video coding schemes, only non-adaptive approaches are used with a constant prediction which continues the boundary samples in the extension region. This causes artifacts in the region of the boundary which may be strongly visible. Especially when 360° video is coded using independently decodable subpictures, extension-related artifacts can occur at all subpicture boundaries and are not limited to the picture boundary area. Thereby, the handling of subpicture boundaries becomes more important. In this paper, an adaptive boundary extension method is investigated with explicit signaling that uses angular prediction for the extension task. It is shown that angular prediction modes are promising candidates for an extension by isolating the impact of the prediction improvement from the signaling cost. The scheme is implemented in the VVC test model, with a simple signaling method that leads to coding gain for over 40% of the subpictures. Preliminary results indicate Bj0ntegaard delta rate savings of about 0.1% when only selected subpictures are considered. This can be considered significant given that only a small area of the prediction signal is affected by the method. A major advantage of the explicit signaling approach is seen in the fact that the encoder can influence the predictions in the boundary region, such that subpicture transitions are more consistent.