In recent years, there has been significant interest in Super-Resolution (SR), which focuses on generating a high-resolution image from a low-resolution input. Deep learning-based methods for super-resolution have been particularly popular and have shown impressive results on various benchmarks. However, research indicates that these methods may not perform as well on strongly compressed videos. We developed a super-resolution benchmark to analyze SR's capacity to upscale compressed videos. Our dataset employed video codecs based on five widely-used compression standards: H.264, H.265, H.266, AV1, and AVS3. We assessed 19 popular SR models using our benchmark and evaluated their ability to restore details and their susceptibility to compression artifacts. To get an accurate perceptual ranking of SR models, we conducted a crowd-sourced side-by-side comparison of their outputs. We found that some SR models, combined with compression, allow us to reduce the video bitrate without significant loss of quality. We also compared a range of image and video quality metrics with subjective scores to evaluate their accuracy on super-resolved compressed videos. The benchmark is publicly available at https://videoprocessing.ai/benchmarks/super-resolution-for-video-compression.html

翻译：近年来，超分辨率技术因专注于从低分辨率输入生成高分辨率图像而受到广泛关注。基于深度学习的超分辨率方法尤其受到青睐，并在多种基准测试中展现出令人印象深刻的效果。然而，研究表明，这些方法在处理强压缩视频时性能可能不佳。我们开发了一个超分辨率基准测试，以分析超分辨率对压缩视频进行上采样的能力。我们的数据集采用了基于五种广泛使用的压缩标准——H.264、H.265、H.266、AV1 和 AVS3——的视频编解码器。我们使用该基准测试评估了 19 个流行的超分辨率模型，考察了它们恢复细节的能力以及对压缩伪影的敏感性。为了获得超分辨率模型的准确感知排序，我们对它们的输出进行了众包并列比较。我们发现，某些超分辨率模型与压缩技术结合，可以在不明显损失质量的前提下降低视频码率。我们还比较了一系列图像与视频质量指标与主观评分，以评估它们在超分辨率压缩视频上的准确性。该基准测试已在 https://videoprocessing.ai/benchmarks/super-resolution-for-video-compression.html 公开提供。