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2 June 2000 Detection of distortion in small moving images compared to the predictions of spatial-temporal model
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Proceedings Volume 3959, Human Vision and Electronic Imaging V; (2000)
Event: Electronic Imaging, 2000, San Jose, CA, United States
The image sequence discrimination model we use models optical blurring and retinal light adaptation. Two parallel channels, sustained and transient, with different masking rules based on contrast gain control, are used. Performance of the model was studied for two tasks representative of a video communication system with versions of monochrome H.263 compressed images. In the first study, five image sequences constituted pairs of non-compressed and compressed images to be discriminated with a 2-alternative-forced-choice method together with a staircase procedure. The thresholds for each subject were calculated. Analysis of variance showed that the differences between the pictures were significant. The model threshold was close to the average of the subjects for each picture, and the model thus predicted these results quite well. In the second study, the effect of transmission errors on the Internet, i.e. packet losses, was tested with the method of constant stimuli. Both reference and comparison image was distorted. The task of the subjects was to judge whether the presented video quality was worse than the initially seen reference video. Two different quality levels of the compressed sequences were simulated. The differences in the thresholds among the different video scenes were to some extent predicted by the model. Category scales indicate that detection of distortions and overall quality judgements are based on different psychological processes.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kjell E. Brunnstroem, Bo N. Schenkman, and Albert J. Ahumada Jr. "Detection of distortion in small moving images compared to the predictions of spatial-temporal model", Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000);


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