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Detection of Distortion in Small Moving Images Compared to the Predictions of Spatio-Temporal Model  (2000)
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The image sequence discrimination model we use models optical blurring and retinal light adaptation. Two parallel processing channels are used and masking rules are based on contrast gain controlTwo 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 evaluation with versions of H.263 compressed monochrome 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 image video quality was worse than the initially seen reference image video. Two different quality levels of the compressed sequences were simulated. Category scales were used for further assessments. The scene-wise correlations between model and subjective data were high, but the model performance was comparable to that of other measures. Predictions of more general nature were not high or were predicted better by other measures.
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H263, image, Internet, loss, model, packet, quality, spatio-temporal, video, vision
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Human Vision and Electronic Imaging IV, eds. B.E. Rogowitz and T.N. Pappas, Proc. Vol. 3959, paper 18, SPIE, San Jose, CA
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Curator: Phil So
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Last Updated: August 15, 2019