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Models/GVSP-UGCVQA-NR (single_scale)

GVSP-UGCVQA-NR (single_scale)

Reported on 9 benchmarks across 3 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

  • Video UnderstandingonMSU NR VQA Database
    KLCC· 2021-06-02
    0.7037
    best: 0.7883 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • Video UnderstandingonMSU NR VQA Database
    PLCC· 2021-06-02
    0.8933
    best: 0.9431 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • Video UnderstandingonMSU NR VQA Database
    SRCC· 2021-06-02
    0.8742
    best: 0.9289 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • VideoonMSU NR VQA Database
    KLCC· 2021-06-02
    0.7037
    best: 0.7883 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • VideoonMSU NR VQA Database
    PLCC· 2021-06-02
    0.8933
    best: 0.9431 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • VideoonMSU NR VQA Database
    SRCC· 2021-06-02
    0.8742
    best: 0.9289 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111

Time Series3 results

  • Video Quality AssessmentonMSU NR VQA Database
    KLCC· 2021-06-02
    0.7037
    best: 0.7883 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • Video Quality AssessmentonMSU NR VQA Database
    PLCC· 2021-06-02
    0.8933
    best: 0.9431 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111
  • Video Quality AssessmentonMSU NR VQA Database
    SRCC· 2021-06-02
    0.8742
    best: 0.9289 (MDTVSFA)
    Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC VideosarXiv:2106.01111