针对流媒体视频压缩内容，实现高效超分辨率重建。
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content
- 提出端到端实时超分辨率框架，兼顾性能与低延迟。
- 在540p→4K和360p→1080p上均实现高帧率与更高画质。
- 专为移动设备优化，参数量与计算量显著降低。
视频超分辨率（VSR）是提升低码率、低分辨率视频质量的关键任务，尤其在流媒体应用中至关重要。尽管已有多种解决方案，但多数方法计算开销大，导致帧率低、功耗高，尤其在移动平台表现不佳。本文整合多种方法，提出面向高性能与低运行时的端到端实时视频超分辨率框架。同时引入新的高质量4K测试集以进一步验证方法有效性。所提方案针对两类应用：540p→4K（x4）作为通用场景，以及360p→1080p（x3）更适配移动端。在两个赛道中，方法均显著减少参数量与乘加操作数（MACs），支持高帧率运行，并在VMAF与PSNR指标上优于插值基线。本报告评估了当前最高效的视频超分辨率方法之一。
原文摘要 · Abstract (English)
Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been developed, they often suffer from high computational demands, resulting in low frame rates (FPS) and poor power efficiency, especially on mobile platforms. In this work, we compile different methods to address these challenges, the solutions are end-to-end real-time video super-resolution frameworks optimized for both high performance and low runtime. We also introduce a new test set of high-quality 4K videos to further validate the approaches. The proposed solutions tackle video up-scaling for two applications: 540p to 4K (x4) as a general case, and 360p to 1080p (x3) more tailored towards mobile devices. In both tracks, the solutions have a reduced number of parameters and operations (MACs), allow high FPS, and improve VMAF and PSNR over interpolation baselines. This report gauges some of the most efficient video super-resolution methods to date.
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