Parameter Dependent Wedge Boundary Switching in AVM

IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 1–5

Abstract

The wedge mode in Alliance of Open Media Video Model (AVM) divides a block into two non-rectangular regions to combine two prediction signals. Rather than employing a sharp transition between these signals, the wedge mode utilizes a gradual transition around the boundary. Currently, there is only one such wedge boundary integrated in the AVM reference software, which is insufficient for handling diverse video content. Recently, an additional relatively smooth boundary has been explored using several signaling schemes. While this adaptive wedge boundary demonstrated promising improvements for short sequences, the signaling overhead associated with it restricted its overall potential. The current work uses this smooth boundary and proposes three simple switching schemes, where the boundary is selected based on specific parameters. While this approach reduces flexibility, it mitigates the requirement for signalling, which has proven to be more beneficial. This provides a PSNR-Y BD-rate gain of up to 0.05% in Random Access (RA) and 0.10% gain in Low Delay (LD) configuration. The improvements are particularly significant for high-resolution classes; in class A1, one scheme achieves 0.38% gain in RA, and another 0.24% gain in LD. A High Level Syntax scheme is presented, which combines the performance of 0.10% in RA and 0.12% in LD by signalling 2 bits per sequence.

Authors 4

1. Priyanka Das Aachen Lehrstuhl für Bildgebung und Bildverarbeitung

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Lehrstuhl für Bildgebung und Bildverarbeitung

2. Xiaoqing Zhu

   Netflix (United States)

   Affiliation as printed

   Netflix Inc.,Los Gatos,CA,USA,95032

3. Tim Claßen Aachen Lehrstuhl für Bildgebung und Bildverarbeitung

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Lehrstuhl für Bildgebung und Bildverarbeitung

4. Mathias Wien Aachen Lehrstuhl für Bildgebung und Bildverarbeitung

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Lehrstuhl für Bildgebung und Bildverarbeitung