针对流媒体超分辨率难题，构建真实数据集并提出高效模型
Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content
- 构建真实流媒体数据集StreamSR，覆盖多类型视频
- 提出EfRLFN模型，在保持速度前提下提升画质
- 微调现有模型在新数据集上表现显著提升
实时超分辨率技术虽已进步，但现有方法难以应对压缩视频的挑战。常用数据集无法反映流媒体真实特性，导致评估不具代表性。为此，我们构建了来自YouTube的综合性数据集StreamSR，涵盖多种视频类型与分辨率，贴近真实流媒体场景。我们对11个先进实时超分辨率模型进行基准测试。同时提出EfRLFN模型，融合高效通道注意力与双曲正切激活函数——这是实时超分辨率中的新颖设计。通过架构优化与复合损失函数设计，显著提升训练收敛性与运行效率。EfRLFN结合现有架构优势，在视觉质量与推理速度间取得更好平衡。最后，我们证明在StreamSR上微调其他模型可带来显著性能提升，并在多个标准基准上实现良好泛化。相关数据、代码与基准已开源。
原文摘要 · Abstract (English)
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 https://github.com/EvgeneyBogatyrev/EfRLFN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。