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

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CVD2014 - A Database for Evaluating No-Reference Video Quality Assessment Algorithms. / Nuutinen, Mikko; Virtanen, Toni; Vaahteranoksa, Mikko; Vuori, Tero; Oittinen, Pirkko; Häkkinen, Jukka.

In: IEEE Transactions on Image Processing, Vol. 25, No. 7, 7464299, 01.07.2016, p. 3073-3086.

Research output: Contribution to journalArticleScientificpeer-review

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Nuutinen, M, Virtanen, T, Vaahteranoksa, M, Vuori, T, Oittinen, P & Häkkinen, J 2016, 'CVD2014 - A Database for Evaluating No-Reference Video Quality Assessment Algorithms', IEEE Transactions on Image Processing, vol. 25, no. 7, 7464299, pp. 3073-3086. https://doi.org/10.1109/TIP.2016.2562513

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Nuutinen, Mikko ; Virtanen, Toni ; Vaahteranoksa, Mikko ; Vuori, Tero ; Oittinen, Pirkko ; Häkkinen, Jukka. / CVD2014 - A Database for Evaluating No-Reference Video Quality Assessment Algorithms. In: IEEE Transactions on Image Processing. 2016 ; Vol. 25, No. 7. pp. 3073-3086.

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@article{340ba98f7dfe4cc9b6bf2cd47f8659c4,
title = "CVD2014 - A Database for Evaluating No-Reference Video Quality Assessment Algorithms",
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 (http://www.helsinki.fi/psychology/groups/visualcognition/).",
keywords = "quality attribute, subjective evaluation, Video camera, video quality algorithm",
author = "Mikko Nuutinen and Toni Virtanen and Mikko Vaahteranoksa and Tero Vuori and Pirkko Oittinen and Jukka H{\"a}kkinen",
year = "2016",
month = "7",
day = "1",
doi = "10.1109/TIP.2016.2562513",
language = "English",
volume = "25",
pages = "3073--3086",
journal = "IEEE Transactions on Image Processing",
issn = "1057-7149",
number = "7",

}

RIS - Download

TY - JOUR

T1 - CVD2014 - A Database for Evaluating No-Reference Video Quality Assessment Algorithms

AU - Nuutinen, Mikko

AU - Virtanen, Toni

AU - Vaahteranoksa, Mikko

AU - Vuori, Tero

AU - Oittinen, Pirkko

AU - Häkkinen, Jukka

PY - 2016/7/1

Y1 - 2016/7/1

N2 - 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/).

AB - 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/).

KW - quality attribute

KW - subjective evaluation

KW - Video camera

KW - video quality algorithm

UR - http://www.scopus.com/inward/record.url?scp=84975282796&partnerID=8YFLogxK

U2 - 10.1109/TIP.2016.2562513

DO - 10.1109/TIP.2016.2562513

M3 - Article

VL - 25

SP - 3073

EP - 3086

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

SN - 1057-7149

IS - 7

M1 - 7464299

ER -

ID: 6454458