Fast Selection of INTRA CTU Partitioning in HEVC Encoders using Artificial Neural Networks
Mateusz Lorkiewicz, Olgierd Stankiewicz, Marek Domański, Hsueh‐Ming Hang, Wen-Hsiao Peng · 2021
In the intra-frame video coding, an image is divided into small blocks, and the actual coding is performed individually in these blocks. In this paper, the process is considered in the context of the widely used HEVC compression, where the optimum choice of the division is crucial for the ratedistortion performance. Unfortunately, the search for such optimum division needs very many operations, and is done on the basis of “try and check” approach in the classic implementations. The idea of the paper is to replace this complex part of the encoder by a neural network, and some variants of the potential neural networks are studied and compared in the paper. For the chosen network, the complexity of the encoder is vastly reduced at the cost of negligible loss in the rate-distortion performance. These features are demonstrated using an extensive set of frames from many test video sequences.