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/).

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Subjective evaluation is used to identify impairment factors of multimedia quality. The final quality is often formulated via quantitative experiments, but this approach has its constraints, as subject's quality interpretations, experiences and quality evaluation criteria are disregarded. To identify these quality evaluation factors, this study examined qualitatively the criteria participants used to evaluate audiovisual video quality. A semi-structured interview was conducted with 60 participants after a subjective audiovisual quality evaluation experiment. The assessment compared several, relatively low audio-video bitrate ratios with five different television contents on mobile device. In the analysis, methodological triangulation (grounded theory, Bayesian networks and correspondence analysis) was applied to approach the qualitative quality. The results showed that the most important evaluation criteria were the factors of visual quality, contents, factors of audio quality, usefulness -followability and audiovisual interaction. Several relations between the quality factors and the similarities between the contents were identified. As a research methodological recommendation, the focus on content and usage related factors need to be further examined to improve the quality evaluation experiments.