Using Decision Trees to Accelerate the H.266/VVC-to-AV1 Video Transcoding

H.266/NVC is the current state-of-the-art video coding format, launched in 2020. Despite its high capability to compress video, including ultra-high definition content, royalty costs may prevent its wide adoption. The AOMedia Video 1 (AV1) format is an alternative with competitive coding efficiency, besides being a royalty-free format. However, migrating legacy content from one format to another is a costly task, which requires long processing times. This work presents a solution for accelerating the H.266/NVC-to-AV1 trans coding based on machine learning. Twelve decision tree models trained with data gathered during the H.266NVC decoding and the AV1 encoding processes are proposed and implemented in the libaom reference software, leading to a complexity reduction of 12.60 % at the cost of coding efficiency losses of 1.81 % on average. To the best of the authors' knowledge, this is the first H.266NVC-to-AV1 trans coding acceleration solution published in the literature.