Image/video coding has been a remarkable research area for both academia and industry for many years. Testing datasets, especially high-quality image/video datasets are desirable for the justified evaluation of coding-related research, practical applications, and standardization activities. We put forward a test dataset namely USTC-TD, which has been successfully adopted in the practical end-to-end image/video coding challenge of the IEEE International Conference on Visual Communications and lmage Processing (VCIP) in 2022 and 2023. USTC-TD contains 40 images at 4K spatial resolution and 10 video sequences at 1080p spatial resolution, featuring various content due to the diverse environmental factors (e.g. scene type, texture, motion, view) and the designed imaging factors (e.g. illumination, lens, shadow). We quantitatively evaluate USTC-TD on different image/video features (spatial, temporal, color, lightness), and compare it with the previous image/video test datasets, which verifies the wider coverage and more diversity of the proposed dataset. We also evaluate both classic standardized and recent learned image/video coding schemes on USTC-TD with PSNR and MS-SSIM, and provide an extensive benchmark for the evaluated schemes. Based on the characteristics and specific design of the proposed test dataset, we analyze the benchmark performance and shed light on the future research and development of image/video coding. All the data are released online: https://esakak.github.io/USTC-TD .

翻译：图像/视频编码多年来一直是学术界与工业界的重要研究领域。测试数据集，尤其是高质量的图像/视频数据集，对于编码相关研究、实际应用及标准化活动的合理评估至关重要。本文提出了一个名为USTC-TD的测试数据集，该数据集已成功应用于2022年与2023年IEEE视觉通信与图像处理国际会议（VCIP）的实际端到端图像/视频编码挑战赛中。USTC-TD包含40幅4K空间分辨率的图像与10段1080p空间分辨率的视频序列，其内容因多样的环境因素（如场景类型、纹理、运动、视角）与设计的成像因素（如光照、镜头、阴影）而具有丰富变化。我们从不同图像/视频特征（空间、时间、色彩、亮度）对USTC-TD进行了定量评估，并与先前的图像/视频测试数据集进行了比较，验证了本数据集更广的覆盖范围与更高的多样性。我们还基于PSNR与MS-SSIM指标，在USTC-TD上评估了经典标准化编码方案与近年基于学习的图像/视频编码方案，并为所评估方案提供了全面的基准测试结果。基于所提出测试数据集的特点与具体设计，我们分析了基准性能，并对图像/视频编码的未来研究与发展方向进行了展望。所有数据均已在线发布：https://esakak.github.io/USTC-TD。