Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content

• URL: http://arxiv.org/abs/2602.11339v2
• Date: Sat, 14 Feb 2026 13:58:35 GMT
• Title: Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content
• Authors: Evgeney Bogatyrev, Khaled Abud, Ivan Molodetskikh, Nikita Alutis, Dmitriy Vatolin,
• Abstract summary: We introduce a comprehensive dataset - StreamSR - sourced from YouTube.<n>We benchmark 11 state-of-the-art real-time super-resolution models to evaluate their performance for the streaming use-case.<n>We propose EfRLFN, an efficient real-time model that integrates Efficient Channel Attention and a hyperbolic tangent activation function.
• Score: 1.1926989512978123
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/EvgeneyBogatyrev/EfRLFN.

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