@inproceedings{10.1145/3508259.3508272,
    author = {Siniukov, Maksim and Antsiferova, Anastasia and Kulikov, Dmitriy and Vatolin, Dmitriy},
    title = {Hacking VMAF and VMAF NEG: Vulnerability to Different Preprocessing Methods},
    year = {2021},
    isbn = {9781450384162},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3508259.3508272},
    doi = {10.1145/3508259.3508272},
    abstract = {Video quality measurement plays a critical role in the development of video processing applications. In this paper, we show how popular quality metrics VMAF and its tuning-resistant version VMAF NEG can be artificially increased by video preprocessing. We propose a pipeline for tuning parameters of processing algorithms which allows to increase VMAF by up to 218.8%. A subjective comparison of preprocessed videos showed that with the majority of methods visual quality drops down or stays unchanged. We show that VMAF NEG scores can also be increased by some preprocessing methods by up to 21.9%.},
    booktitle = {2021 4th Artificial Intelligence and Cloud Computing Conference},
    pages = {89–96},
    numpages = {8},
    keywords = {video quality measurement, VMAF, video quality, video codecs comparisons, quality improvement, objective full-reference metric, codecs tuning},
    location = {Kyoto, Japan},
    series = {AICCC '21}
}