Fast Partitioning for VVC Intra-Picture Encoding with a CNN Minimizing the Rate-Distortion-Time Cost

Abstract

This paper presents a CNN to reduce the encoding time of a VVC-based intra-picture encoder. For encoding a 32 × 32 block, the CNN estimates two partitioning parameters that restrict the allowed coding block width and height. To estimate them such that the encoder skips testing inefficient partitioning modes, we train the CNN as follows: First, we generate training data by encoding sequences without the CNN. While encoding, we test all combinations of the two parameters for each 32 × 32 block and store the resulting Lagrangian rate-distortion-time (RDT) cost. We use the recorded cost to derive the loss function when training the CNN. Consequently, the CNN is trained such that it minimizes the Lagrangian RDT cost. Our CNN reduces the encoding time by 50% with a bit rate increase of 0.9%, which outperforms existing CNN-based approaches. Our generic training approach could also be applied for other encoder parameters.