USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s
Zhuoyuan Li, Junqi Liao, Chuanbo Tang, Haotian Zhang, Yuqi Li, Yifan, Bian, Xihua Sheng, Xinmin Feng, Yao Li, Changsheng Gao, Li Li, Dong Liu, Feng, Wu

TL;DR
The paper introduces USTC-TD, a new high-quality image and video dataset for evaluating coding schemes, and provides a comprehensive benchmark analysis to guide future research in image/video coding.
Contribution
It presents a diverse, high-resolution dataset for image/video coding evaluation and offers extensive benchmarking of existing coding schemes using multiple quality metrics.
Findings
USTC-TD effectively addresses shortcomings of previous datasets.
Benchmark results highlight strengths and weaknesses of current coding schemes.
The dataset and benchmarks facilitate future research in image/video coding.
Abstract
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 Image 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,…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Video Coding and Compression Technologies
