FANVIDv2：在复合退化下通过人脸和车牌识别评估视频超分辨率

中文摘要 AI 辅助

视频超分辨率（VSR）通常通过在双三次下采样的片段上计算PSNR和SSIM来评判，尽管在监控场景中，其目的是使人脸和车牌变得“可识别”。我们提出了FANVIDv2，一个通过识别管道对其输出所能完成的任务来给VSR评分的基准。FANVIDv2提供了320×180分辨率的低分辨率（LR）片段，并带有48位公众人物（每人一张高分辨率画廊图像）和375个车牌片段（覆盖360个不同车牌字符串）的高分辨率（HR）参考。LR片段是通过随机复合退化（模糊、缩放抖动、传感器噪声、JPEG压缩、最终下采样）生成的，而非仅使用双三次下采样。两个指标对检测区域内的识别进行评分：FaceRecBox仅在人脸被定位并正确识别时给予奖励，而TextRecBox通过按定位质量加权的归一化编辑距离对车牌转录进行评分。使用一个2.3M参数的VSR基线（RCDM），FaceRecBox从0.6864提升到0.7222，匹配人脸的识别准确率从84.35%提升到86.93%，TextRecBox从0.3088提升到0.3667；一个残差图门控变体（RCDM-RMGF）在车牌上达到0.3801。我们详细描述了退化模型、基线架构和评分器，并发布了标注、元数据、下载和退化脚本以及评估代码。

英文摘要

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 benchmark that scores VSR by what a recognition pipeline can do with its output. FANVIDv2 provides $320\times180$ low-resolution (LR) clips with high-resolution (HR) references for 48 public figures (with one HR gallery image each) and 375 licence-plate clips covering 360 distinct plate strings. LR clips are generated with a randomised compound degradation (blur, resize jitter, sensor noise, JPEG compression, final downsampling) rather than bicubic downsampling alone. Two metrics score recognition \emph{inside} detections: FaceRecBox rewards a face only if it is localised and correctly identified, and TextRecBox scores plate transcriptions by normalised edit distance weighted by localisation quality. With a 2.3\,M-parameter VSR baseline (RCDM), FaceRecBox rises from 0.6864 to 0.7222, identity accuracy on matched faces from 84.35\% to 86.93\%, and TextRecBox from 0.3088 to 0.3667; a residual-map gated variant (RCDM-RMGF) reaches 0.3801 on plates. We describe the degradation model, the baseline architectures and the scorers in detail, and release annotations, metadata, download and degradation scripts and evaluation code.

发表机构

• Indian Institute of Technology Bombay（印度理工学院孟买分校）

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