TY  - JOUR
T1  - VONV: NR-VMAF Based Optimization for VSR Quality Assurance and Thermal Throttling Mitigation
AU  - Park, Hye-jin 
AU  - Ha, Rhan 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.9.1457
KW  - Super Resolution
KW  - Thermal Management
KW  - Optimization
KW  - On-Device AI
KW  - Mobile System
AB  - On-Device VSR(Video Super Resolution) is challenged by the high computational load of Deep Neural Networks(DNNs), causing heat and performance degradation. Adaptive VSR offers a solution by optimizing performance, but it must also take a balanced approach that considers not only maintaining performance but also ensuring visual quality. This paper proposes VONV(VSR Optimization with NR-VMAF), an adaptive VSR method that uses NR(No-Reference)-VMAF to selectively apply DNN-based SR or bicubic interpolation per frame, in order to mitigate thermal throttling while maintaining consistent video quality. NR-VMAF demonstrates high accuracy, staying within 2 points of FR-VMAF, validating its reliability. Tests on two devices with distinct performance profiles showed VONV extends the thermal throttling threshold threefold and reduces overall inference time by 1.4 to 4 times. These results demonstrate that VONV, by using NR-VMAF, effectively optimizes both quality and performance in mobile environments.