Low Complexity Super Resolution for Resampling-based Video Coding

Resampling-based video coding is a well-established strategy for compressing high-resolution video under limited bandwidth. Recently, super resolution (SR) technologies has been explored for upsampling the low-resolution reconstructed video in the decoder side. Despite the promising rate-distortion improvements these SR model can offer, the significant complexity of neural network makes them impractical for real-world applications. In this paper, we propose a low complexity SR model for resampling-based video coding by applying CP decomposition to vanilla convolution to reduce complexity. To enhance the performance of the proposed low complexity SR, we utilize multiple decoded information generated during compression as additional auxiliary input of SR. This information provides valuable insights of the compressed picture, such as the texture and directional details. Additionally, to minimize the number of parameters in the neural network, a single model with split branches is designed to process luma and chroma components separately. Experimental results demonstrate that the proposed method achieves 3.72% and 3.65% BD-rate savings under random access and all-intra configurations compared to the latest video coding standard VVC/H.266, respectively. Compared to existing works, the proposed SR model exhibits a superior trade-off between complexity and coding performance.