Minconvnets: A New Class of Multiplication-Less Neural Networks

The Versatile Video Coding (VVC) standard introduces new types of frame partitioning structures, such as QuadTrees (QT) and Multi-Type Trees (MTT). To achieve the best compression efficiency, for each block of pixels the encoder performs a recursive search over the partitioning possibilities, which also impacts significantly on the encoding complexity and processing time. This work proposes a machine learning-based solution for quick block partitioning decisions. A set of fourteen Random Forests were trained using data gathered during the encoding process and the models were employed to decide whether vertical and horizontal partitions are required for each candidate block. The proposed solution leads to an average encoding time reduction of 34.76% at the cost of a compression efficiency loss of 1.03%.