CVD2014-A Database for Evaluating No-Reference Video Quality Assessment Algorithms
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. CVD...
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| Published in | IEEE transactions on image processing Vol. 25; no. 7; pp. 3073 - 3086 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1057-7149 1941-0042 1941-0042 |
| DOI | 10.1109/TIP.2016.2562513 |
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| Abstract | 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 (<;uri xlink:type="simple">http://www.helsinki.fi/psychology/groups/visualcognition/<;/uri>). |
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| AbstractList | 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 (http://www.helsinki.fi/psychology/groups/visualcognition/). 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 (<;uri xlink:type="simple">http://www.helsinki.fi/psychology/groups/visualcognition/<;/uri>). |
| Author | Nuutinen, Mikko Vaahteranoksa, Mikko Vuori, Tero Oittinen, Pirkko Hakkinen, Jukka Virtanen, Toni |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27164589$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Cameras Descriptions Distortion Image processing Observers Quality Quality assessment quality attribute Spatial databases subjective evaluation Video camera Video compression video quality algorithm Video sequences |
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| Title | CVD2014-A Database for Evaluating No-Reference Video Quality Assessment Algorithms |
| URI | https://ieeexplore.ieee.org/document/7464299 https://www.ncbi.nlm.nih.gov/pubmed/27164589 https://www.proquest.com/docview/1790686408 https://www.proquest.com/docview/1791323563 https://www.proquest.com/docview/1816002494 https://doi.org/10.5281/zenodo.2646315 |
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