Abstract
Recent advancements in real-time super-resolution have enabled higher-quality video streaming, yet existing methods struggle with the unique challenges of compressed video content. Commonly used datasets do not accurately reflect the characteristics of streaming media, limiting the relevance of current benchmarks. To address this gap, we introduce a comprehensive dataset - StreamSR - sourced from YouTube, covering a wide range of video genres and resolutions representative of real-world streaming scenarios. We benchmark 11 state-of-the-art real-time super-resolution models to evaluate their performance for the streaming use-case. Furthermore, we propose EfRLFN, an efficient real-time model that integrates Efficient Channel Attention and a hyperbolic tangent activation function - a novel design choice in the context of real-time super-resolution. We extensively optimized the architecture to maximize efficiency and designed a composite loss function that improves training convergence. EfRLFN combines the strengths of existing architectures while improving both visual quality and runtime performance. Finally, we show that fine-tuning other models on our dataset results in significant performance gains that generalize well across various standard benchmarks. We made the dataset, the code, and the benchmark available at this https URL.
Abstract (translated)
最近的实时超分辨率技术进步已经实现了更高质量的视频流传输，然而现有的方法在处理压缩视频内容的独特挑战时显得力不从心。常用的数据库不能准确反映流媒体的特点，限制了当前基准测试的相关性。为了解决这一缺口，我们引入了一个全面的数据集——StreamSR，该数据集来源于YouTube，涵盖了各种代表现实世界流媒体场景的视频类型和分辨率。我们在11种最新的实时超分辨率模型上进行了基准测试，以评估它们在流媒体使用情况下的性能。 此外，我们提出了一种高效的实时模型EfRLFN，它结合了有效的通道注意机制（Efficient Channel Attention）以及双曲正切激活函数（hyperbolic tangent activation function），这是一种在实时超分辨率上下文中的新颖设计选择。我们对架构进行了广泛的优化以最大化效率，并设计了一个复合损失函数来改善训练的收敛性。EfRLFN综合了现有架构的优点，同时提升了视觉质量和运行时性能。 最后，我们证明，在我们的数据集上微调其他模型可以实现显著的性能提升，这些提升在各种标准基准测试中表现良好且具有很好的泛化能力。我们在以下网址提供了该数据集、代码和基准测试结果：[URL]（请将[URL]替换为实际提供的链接地址）。
URL
https://arxiv.org/abs/2602.11339
