@article{M6F64CB2C,
title = "VONV: NR-VMAF Based Optimization for VSR Quality Assurance and Thermal Throttling Mitigation",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.9.1457",
author = "Hye-jin Park, Rhan Ha",
keywords = "Super Resolution, Thermal Management, Optimization, On-Device AI, Mobile System",
abstract = "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."
}