Stephen D. Voran and Stephen Wolf

Abstract: The ability of the human eye to resolve detail in a video scene is related to how much motion is present at the point of focus and whether or not the eye can track the motion. Thus, stationary portions of the video scene can be resolved in great detail by the eye, while moving portions of the video scene are normally resolved in less detail (provided the eye cannot fully track the motion). Low bit-rate digital video channels (e.g., video teleconferencing) determine how many bits are used for each local area of the video scene. Since the time averaged information content of a still video scene is much less than the time averaged information content of a moving scene, typical video teleconferencing channels can have very different static and dynamic responses. The dynamic response of the channel is also a function of the video scene and can vary on a frame-by-frame basis. Thus, it is desirable to have a general algorithm (applicable to any test waveform or test scene) that can separate the dynamic response from the static response on a frame by frame basis. This contribution describes one such algorithm. The motion-still segmentation algorithm presented here can be applied to any test waveform or test scene in order to separate the moving portions from the still portions of the video scene. A detailed description of the motion-still segmentation algorithm and its theoretical basis is given first. Then, a typical application of the algorithm is presented: measuring the increased spatial blurring of moving objects in an actual video teleconferencing scene. American National Standards Institute (ANSI), Motion-Still Segmentation Algorithm For VTC/VT Objective Quality Assessment: T1Q1.5/91-110, approved January 22, 1991

Keywords: video teleconferencing; video telephony; subjective quality; Objective Quality; dynamic resolution; static resolution

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