RivuletMLP: An MLP-based Architecture for Efficient Compressed Video Quality Enhancement

Abstract

Quality degradation from video compression manifests both spatially along texture edges and temporally with continuous motion changes. Despite recent advances, extracting aligned spatiotemporal information from adjacent frames remains challenging. This is mainly due to limitations in receptive field size and computational complexity, which makes existing methods struggle to efficiently enhance video quality. To address this issue, we propose RivuletMLP, an MLP-based network architecture. Specifically, our framework first employs a Dynamically Guided Deformable Alignment (DDA) module to adaptively explore and align multi-frame feature information. Subsequently, we introduce two modules for feature reconstruction: a Spatiotemporal Feature Flow (SFF) Module and a Benign Selection Compensation (BSC) module. The SFF module establishes non-local dependencies through an innovative feature permutation mechanism. Additionally, the BSC module utilizes a collaborative strategy of deep feature extraction and local region refinement to alleviate inter frame motion discontinuity caused by compression. Experimental results demonstrate that RivuletMLP achieves superior computational efficiency while maintaining powerful reconstruction capabilities.