CVD2014 - A Database for Evaluating No-Reference Video Quality Assessment Algorithms

Mikko Nuutinen, Toni Virtanen, Mikko Vaahteranoksa, Tero Vuori, Pirkko Oittinen, Jukka Häkkinen

Research output: Contribution to journalArticleScientificpeer-review

69 Citations (Scopus)


In this paper, we present a new video database: CVD2014 - Camera Video Database. In contrast to previous video databases, this database uses real cameras rather than introducing distortions via post-processing, which results in a complex distortion space in regard to the video acquisition process. CVD2014 contains a total of 234 videos that are recorded using 78 different cameras. Moreover, this database contains the observer-specific quality evaluation scores rather than only providing mean opinion scores. We have also collected open-ended quality descriptions that are provided by the observers. These descriptions were used to define the quality dimensions for the videos in CVD2014. The dimensions included sharpness, graininess, color balance, darkness, and jerkiness. At the end of this paper, a performance study of image and video quality algorithms for predicting the subjective video quality is reported. For this performance study, we proposed a new performance measure that accounts for observer variance. The performance study revealed that there is room for improvement regarding the video quality assessment algorithms. The CVD2014 video database has been made publicly available for the research community. All video sequences and corresponding subjective ratings can be obtained from the CVD2014 project page (

Original languageEnglish
Article number7464299
Pages (from-to)3073-3086
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number7
Publication statusPublished - 1 Jul 2016
MoE publication typeA1 Journal article-refereed


  • quality attribute
  • subjective evaluation
  • Video camera
  • video quality algorithm


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