This study presents a methodology of forming contextually valid scales for subjective video quality measurement. Any
single value of quality e.g. Mean Opinion Score (MOS) can have multiple underlying causes. Hence this kind of a
quality measure is not enough for example, in describing the performance of a video capturing device. By applying
Interpretation Based Quality (IBQ) method as a qualitative/quantitative approach we have collected attributes familiar to the end user and that are extracted directly from the material offered by the observers' comments. Based on these
findings we formed contextually valid assessment scales from the typically used quality attributes. A large set of data
was collected from 138 observers to generate the video quality vocabulary. Video material was shot by three types of
video cameras: Digital video cameras (4), digital still cameras (9) and mobile phone cameras (9). From the quality
vocabulary, we formed 8 unipolar 11-point scales to get better insight of video quality. Viewing conditions were adjusted
to meet the ITU-T Rec. P.910 requirements. It is suggested that the applied qualitative/quantitative approach is especially
efficient for finding image quality differences in video material where the quality variations are multidimensional in
nature and especially when image quality is rather high.
We present an effective method for comparing subjective audiovisual quality and the features related to the quality
changes of different video cameras. Both quantitative estimation of overall quality and qualitative description of critical
quality features are achieved by the method. The aim was to combine two image quality evaluation methods, the
quantitative Absolute Category Rating (ACR) method with hidden reference removal and the qualitative Interpretation-
Based Quality (IBQ) method in order to see how they complement each other in audiovisual quality estimation tasks. 26
observers estimated the audiovisual quality of six different cameras, mainly mobile phone video cameras. In order to
achieve an efficient subjective estimation of audiovisual quality, only two contents with different quality requirements
were recorded with each camera. The results show that the subjectively important quality features were more related to
the overall estimations of cameras' visual video quality than to the features related to sound. The data demonstrated two
significant quality dimensions related to visual quality: darkness and sharpness. We conclude that the qualitative
methodology can complement quantitative quality estimations also with audiovisual material. The IBQ approach is
valuable especially, when the induced quality changes are multidimensional.
Noise decreases video quality considerably, particularly in dark environments. In a video clip, noise can be seen as an
unwanted spatial or temporal variation in pixel values. The object of the study was to find a threshold value for signal-to-noise
ratio (SNR) in which the video quality is perceived to be good enough. Different illumination levels for video
shooting were studied using both subjective and objective (SNR measurements) methodologies. Five camcorders were
selected to cover different sensor technologies, recording formats and price categories. The test material for the
subjective test was recorded in an environment simulator, where it was possible to adjust lighting levels. Double
staircase test was used as the subjective test method. The test videos for objective measurements were recorded using an
ISO 15739 based environment. There was a correlation found between objective and subjective measurements, between
measured SNR and perceived quality. Good enough video quality was reached between SNR values of 15.3 dB and 17.2
dB. With 3CCD and super HAD-CCD technologies, video quality was brighter, less noisy, and the SNR was better in
low light conditions compared to the quality with conventional CCDs.
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