ELIM: Extremely Low-Complexity Implicit Neural Model for Super Resolution-Based Coding

The super-resolution (SR)-based coding, which encodes a frame at a reduced resolution to achieve a lower bitrate, is a prevalent tool used in modern video coding standards. Accordingly, the low-resolution frame is restored to the full resolution at the reconstruction stage for subsequent reference. Therefore, the resolution restoration algorithm significantly affects the coding performance. This paper devises a highly efficient and extremely low complexity implicit neural model (ELIM) for SR-based encoding to support arbitrary scale factors. Specifically, ELIM consists of two stages: Feature Aggregation and Coordinate Upsampling. In Feature Aggregation, we embed a simplified attention block to the U-Net style framework to collect valuable information while reducing computational complexity through downsampling. In Coordinate Upsampling, in addition to the extracted content features, information including coordinate relative location and pixel cell size is fused to achieve better performance. We exemplify ELIM on the AV2 codec (the next generation of AV1). Extensive experiments demonstrate its superior performance: it achieves 4.17% BD-Rate gains over the anchor AV2 reference software with only 5,185 flops/pixel, significantly surpassing existing methods. The low complexity of ELIM is attractive to real applications.