NN-based decision predictor of picture resolution change for video coding
Hassane Guermoud, Philippe Bordes, Franck Galpin, Thierry Dumas, Kevin Reuzé, Édouard François, Gagan Rath · 2024
Reference Picture Resampling (RPR) is a powerful tool that allows improving video coding efficiency of next generation codecs like Versatile Video Coding (VVC) or Enhanced Compression Model (ECM). This feature is well designed to support frame changing resolution without inserting an instantaneous decoder refresh (IDR) or intra random access picture (IRAP). Video streaming and low delay scenarios can take advantage of RPR to ensure a smooth frame-based bit-rate adaptation, compared to traditional techniques that can generate bitrate leaps. This paper proposes an encoder method to select the picture resolution change parameters effectively depending on the video signal characteristics. The picture resolution change decision is based on a low complexity neural network, and it is performed before the encoding process without RD-score computations making this approach suitable for real time and low delay implementation. The experiments under Random Access (RA) and All Intra (AI) configurations of the VVC Test Model (VTM-21.1) show that the proposed method can bring luma BD-rate gain improvement of 1.46% and 0.95% respectively compared to the VVC Test Model anchor.