IFIC3A-VSR: A Computational Accelerated Video Super-Resolution Network Based on Inter-frame Information Complexity Classification

The increasing demand for high-resolution images across various fields such as medicine, remote sensing, and security has driven the development of video super-resolution (VSR) technology. This article delves into the computational challenges and real-time performance issues associated with current VSR models, which often struggle with the high computational requirements of processing large numbers of video frames. The authors present a novel computational accelerated VSR network based on inter-frame information complexity classification (IFIC3A-VSR), which addresses these challenges by intelligently categorizing the complexity of inter-frame information and selecting appropriate super-resolution reconstruction branches. The network incorporates a self-calibrated deformable 3D convolution (SCdcn) to enhance feature extraction and two lightweight attention modules to optimize convolutional kernels and channels. Extensive experiments demonstrate that IFIC3A-VSR reduces computational complexity by 35% compared to mainstream algorithms while maintaining outstanding VSR performance. The article provides a detailed comparison with state-of-the-art models, highlighting the superior efficiency and accuracy of the proposed method. Additionally, it discusses the potential for future improvements, such as incorporating Transformer-based video classifiers to enhance classification adaptability and accuracy.