VONV: NR-VMAF Based Optimization for VSR Quality Assurance and Thermal Throttling Mitigation 


Vol. 50,  No. 9, pp. 1457-1465, Sep.  2025
10.7840/kics.2025.50.9.1457


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  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.

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[IEEE Style]

H. Park and R. Ha, "VONV: NR-VMAF Based Optimization for VSR Quality Assurance and Thermal Throttling Mitigation," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 9, pp. 1457-1465, 2025. DOI: 10.7840/kics.2025.50.9.1457.

[ACM Style]

Hye-jin Park and Rhan Ha. 2025. VONV: NR-VMAF Based Optimization for VSR Quality Assurance and Thermal Throttling Mitigation. The Journal of Korean Institute of Communications and Information Sciences, 50, 9, (2025), 1457-1465. DOI: 10.7840/kics.2025.50.9.1457.

[KICS Style]

Hye-jin Park and Rhan Ha, "VONV: NR-VMAF Based Optimization for VSR Quality Assurance and Thermal Throttling Mitigation," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 9, pp. 1457-1465, 9. 2025. (https://doi.org/10.7840/kics.2025.50.9.1457)
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