Paper
30 May 2002 Quality of video affected by packet loss distortion compared to the predictions of a spatio-temporal model
Kjell E. Brunnstrom, Bo N. Schenkman
Author Affiliations +
Proceedings Volume 4662, Human Vision and Electronic Imaging VII; (2002) https://doi.org/10.1117/12.469511
Event: Electronic Imaging, 2002, San Jose, California, United States
Abstract
Image quality of images as judged by human observers may be determined by different simulation models. The results of an image discrimination model is discussed and compared to the results of the peak signal-to-noise ratio. The image discrimination model simulates how people analyze spatial-temporal information and it predicts detection of distortion. Peak signal-to-noise ratio measures the physical difference between an original and a distorted image sequence. To test if the model could without modifications be used to predict image quality in small moving images, a perceptual experiment was carried out with ten observers. Image quality judgments were measured with five different video scenes using eight category scales and magnitude estimations. Packet loss of transmissions was simulated for two H.263 coders, one with two layers and one with only one layer. The reliability of the judgments was generally high. The judged image quality depended on type of scene and coder. There was a strong inter-correlation between the category scales. Both magnitude estimations of image quality and ratings of a category scale for image quality could to some extent be predicted by the model, but there were no advantages for the visual model.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kjell E. Brunnstrom and Bo N. Schenkman "Quality of video affected by packet loss distortion compared to the predictions of a spatio-temporal model", Proc. SPIE 4662, Human Vision and Electronic Imaging VII, (30 May 2002); https://doi.org/10.1117/12.469511
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Cited by 3 scholarly publications.
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KEYWORDS
Image quality

Video

Visual process modeling

Visualization

Distortion

Data modeling

Reliability

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