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In [9], a rate control technique for offline processing using a video quality metric and evolution strategy was proposed; however, this scheme is still computationally complex.
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The ITU-T standardized G.1070 video quality model is widely used as a video quality planning tool for video conferencing applications.
It is worth noting that the DASH solution uses a video signal quality level determined at the users' devices.
We conduct extensive evaluations using a perceptual video quality metric as well as the PSNR in which we compare the visual quality predicted based on the advanced video traces with the visual quality determined from experiments with actual video.
The untrained operators in this trial were able to obtain diagnostic quality echocardiographic images using a video-based, instructional program delivered just-in-time through video glasses.
The utility is based on using an objective video quality measure to value client satisfaction across different possible adaptations of the video layers.
According to this model, the video quality score can be computed using the video quality parameters defined for the terminals and networks employed in the video delivering.
We compare our approach with the block-wise non-local temporal denoising approach for TOF images of [10] and one of the best performing video denoising methods today VBM3D [27] using objective video quality measures (PSNR and MSE) and visual quality comparison.
In Section 4, we assign values to the client utility parameters in our formulated problem using a recently developed video quality metric.
In a typical subjective quality assessment study, test subjects are asked to rate the test video using a given quality scale.
The recording was done in an office environment using a broadcast quality digital video camera.
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