Residue super-resolution convolutional neural network based complexity reduction for H.266 VVC intra-coding

Abstract

Versatile Video Coding (VVC) promised to provide the same video quality as HEVC with 50 % bitrate reduction, which was introduced in 2020. Our suggested method for VVC Intra-coding is residue super-resolution convolutional neural network (RSR-CNN) utilizing downsampling and upsampling procedures. We present an effective complexity reduced VVC intra-coding scheme based on residue SR-CNN. Reducing an original video's resolution in both the vertical and horizontal directions is all that is required to execute down sampling. Increasing the video dimensions for improved visual quality, convolutional neural networks are utilized in the upsampling process to create residue super-resolution. Specifically, for every block, we train a CNN model to perform residue SR after downsampling and compressing the residue at low resolution, and then we carry out motion estimation (ME) and motion compensation (MC) to extract the residue. Using the MC prediction signal, a new residue SR-CNN is designed. Additionally, this work comprehensively examines the complexity and performance of VVC intra-coding tools and integrates them with the residue SR-CNN method. The experiments demonstrate a substantial time savings of 40 % in encoding with BDBR coding gains of 4.2 %, and 2.9 % in AI and RA configurations respectively.