2021/07/09 by Maksim Siniukov, Siniukov, Maksim, Anastasia Antsiferova +5 · 3 citations
Computer Science · Engineering · #Advanced Image Processing Techniques #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer security #Computer vision #Data pre-processing #Engineering #FOS: Computer and information sciences #Graphics (cs.GR) #Image Enhancement Techniques #Image and Video Quality Assessment #Metric (unit) #Multimedia (cs.MM) #Pipeline (software) #Preprocessor #Quality (philosophy) #Video processing #Video quality #Vulnerability (computing) #cs.CV #cs.GR #cs.MM
paper · pdf · doi:10.48550/arxiv.2107.04510
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/07/09 · arxiv created 2021/08/16 · arxiv updated 2021/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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%.