Efficient Real-Time On-Mobile Video Super-Resolution with Automatic Evolutionary Neural Architecture Search

Xuncheng Liu, Weizhan Zhang, Caixia Yan, Zhiwen Wang, Haipeng Du

Abstract: Real-time Video Super-Resolution (VSR) on mobile devices requires balancing sub-33ms latency with quality preservation – a dual challenge that existing NAS methods fail to address due to restrictive search spaces and unproven convergence. We propose an evolutionary NAS framework with three key innovations: (1) An Almost Sure Strong (A.S.S.)-convergent Genetic Algorithm with theoretical guarantees; (2) A bidirectional gene-architecture mapping encoding topological and parametric interdependencies; (3) Automated gene-to-model compilation through repair-reduction rules. Our hardware-aware implementation achieves state-of-the-art performance (+111.8% composite score) on the REDS dataset while reducing latency by 2\(\times \) compared to baseline architectures. The convergence-proven GA demonstrates superior optimization stability over conventional NAS approaches, establishing new Pareto frontiers for mobile VSR deployments.

External IDs:dblp:conf/icann/LiuZYWD25