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Conference Paper
Convolutional Neural Network-based Generation and Enhancement for Inter Prediction of Versatile Video Coding (VVC)
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- Authors
- Young-Ju Choi, Jongho Kim, Sung-Chang Lim, Jin Soo Choi, Byung-Gyu Kim
- Issue Date
- 2023-07
- Citation
- International Conference on Multimedia Information Technology and Applications (MITA) 2023, pp.1-4
- Language
- English
- Type
- Conference Paper
- Abstract
- Inspired by the success of neural network-based approaches in computer vision, research on neural networkbased video coding have emerged. With the aim of achieving improved compression efficiency, an investigation on inter prediction plays a crucial role in neural network-based video coding. In this paper, we propose a convolutional neural network (CNN)-based generation and enhancement method for inter prediction (GEIP) in the Versatile Video Coding (VVC) standard. By leveraging fused features and self-attended features based on attention mechanism, the proposed method maximizes inter prediction performance. When compared with VTM-11.0 NNVC1.0 anchor, it is verified that the BD-rate reduction of the proposed method can be up to 7.06% on Y component under random access (RA) configuration.
- KSP Keywords
- Attention mechanism, Computer Vision(CV), Convolution neural network(CNN), Enhancement method, Fused features, Inter prediction, Random Access, Video coding, compression efficiency, network-based, prediction performance