Learning-based encoder algorithms for VVC in the context of the optimized VVenC implementation

Versatile Video Coding (VVC) is the most recent and efficient video-compression standard of ITU-T and ISO/IEC. It follows the principle of a hybrid, block-based video codec and offers a high flexibility to select a coded representation of a video. While encoders can exploit this flexibility for compression efficiency, designing algorithms for fast encoding becomes a challenging problem. This problem has recently been attacked with data-driven methods that train suitable neural networks to steer the encoder decisions. On the other hand, an optimized and fast VVC software implementation is provided by Fraunhofer's Versatile Video Encoder VVenC. The goal of this paper is to investigate whether these two approaches can be combined. To this end, we exemplarily incorporate a recent CNN-based approach that showed its efficiency for intra-picture coding in the VVC reference software VTM to VVenC. The CNN estimates parameters that restrict the multi-type tree (MTT) partitioning modes that are tested in rate-distortion optimization. To train the CNN, the approach considers the Lagrangian rate-distortion-time cost caused by the parameters. For performance evaluation, we compare the five operational points reachable with the VVenC presets to operational points that we reach by using the CNN jointly with the presets. Results show that the combination of both approaches is efficient and that there is room for further improvements.