mlsanityData to 7 Oct 2026

Hacking VMAF and VMAF NEG: vulnerability to different preprocessing methods

arXiv 2107.04510 ↗First posted 9 Jul 2021cs.MM cs.CV cs.GRcited by 8

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Abstract (arXiv)

Video-quality measurement plays a critical role in the development of video-processing applications. In this paper, we show how video preprocessing can artificially increase the popular quality metric VMAF and its tuning-resistant version, VMAF NEG. We propose a pipeline that tunes processing-algorithm parameters to increase VMAF by up to 218.8%. A subjective comparison revealed that for most preprocessing methods, a video's visual quality drops or stays unchanged. We also show that some preprocessing methods can increase VMAF NEG scores by up to 23.6%.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

3 of 20 references.

References · 3

  1. 2019ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compressioncited by 24
  2. 2019Hacking VMAF with Video Color and Contrast Distortioncited by 8
  3. 2019Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQEcited by 3

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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 3.

8 papers.