English(EN) FANVIDv2: Evaluating Video Super-Resolution by Face and Licence-Plate Recognition Under Compound Degradation

新的基准FANVIDv2通过面部和车牌识别评估视频超分辨率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员推出了FANVIDv2，这是一个新的基准，旨在根据面部和车牌的可识别性来评估视频超分辨率（VSR）性能，而不是传统的PSNR和SSIM等指标。该基准通过对低分辨率视频片段应用模糊、噪声和压缩等复合退化，模拟了现实的监控条件。FANVIDv2包含专门的评分指标，FaceRecBox用于身份识别，TextRecBox用于车牌转录准确性，从而对VSR系统进行更实际的评估。 AI

影响该基准可以通过优先考虑可识别性而非抽象的图像质量指标，推动VSR模型朝着在监控和安全领域的实际应用发展。

排序理由该项目是一篇介绍新基准和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Kavitha Viswanathan, Vrinda Goel, Shlesh Gholap, Devayan Ghosh, Madhav Gupta, Dhruvi Ganatra, Sanket Potdar, Amit Sethi · 2026-10-01 04:00

FANVIDv2：通过面部和车牌识别评估复合退化下的视频超分辨率

arXiv:2609.39649v1 Announce Type: new Abstract: Video super-resolution (VSR) is normally judged by PSNR and SSIM on clips that were downsampled bicubically, although in surveillance its purpose is to make faces and licence plates \emph{recognisable}. We present FANVIDv2, a benchm…

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FANVIDv2：通过面部和车牌识别评估复合退化下的视频超分辨率

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