@inproceedings{43eb006b4b2940c0b3696da882eb0c85,
title = "A Synthetic Video Dataset for Video Compression Evaluation",
abstract = "In this paper, a new Synthetic video Texture dataset (SynTex) is introduced. It was generated using a Computer Graphics Imagery (CGI) environment and offers the capability of being able to generate many versions of the same scenes with different video parameters. This will support research in video compression enabling researchers to understand and model the relationship between video content and its coding parameters. To validate that SynTex is suitable for this purpose, firstly, typical spatio-temporal descriptors were calculated and compared against existing real video datasets with similar parameters. Then, the encoding statistics of SynTex were extracted using the HEVC reference software and compared to natural video datasets. The comparisons show that SynTex exhibits a comparable coverage over the spatial and temporal domain and it has similar encoding statistics to real video datasets.",
keywords = "Synthetic Video Dataset, Video Texture, Video Compression, HEVC Coding Statistics",
author = "Di Ma and Angeliki Katsenou and David Bull",
year = "2019",
month = aug,
day = "26",
doi = "10.1109/ICIP.2019.8803798",
language = "English",
isbn = "9781538662502",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2019 26th IEEE International Conference on Image Processing (ICIP 2019)",
address = "United States",
}