For structured-light range imaging, color stripes can be used for increasing the number of distinguishable light patterns compared to binary BW stripes. Therefore, an appropriate use of color patterns can reduce the number of light projections and range imaging is achievable in single video frame or in "one shot". On the other hand, the reliability and range resolution attainable from color stripes is generally lower than those from multiply projected binary BW patterns since color contrast is affected by object color reflectance and ambient light. This paper presents new methods for selecting stripe colors and designing multiple-stripe patterns for "one-shot" and "two-shot" imaging. We show that maximizing color contrast between the stripes in one-shot imaging reduces the ambiguities resulting from colored object surfaces and limitations in sensor/projector resolution. Two-shot imaging adds an extra video frame and maximizes the color contrast between the first and second video frames to diminish the ambiguities even further. Experimental results demonstrate the effectiveness of the presented one-shot and two-shot color-stripe imaging schemes.
Abstract-High Efficiency Video Coding (HEVC) is the state-of-the-art video coding standard. It adopts a hierarchical quad-tree based coding unit (CU) partitioning structure that is flexible in various texture and motion characteristics of a video signal. However, the exhaustive partitioning process for finding optimal CU partitions requires a dramatic increase in computational complexity of HEVC encoder compared with previous video coding standards. In this paper, a fast CU partitioning algorithm is proposed for HEVC encoder, which early terminates the CU partitioning process based on the Bayesian decision rule using joint online and offline learning. An online learning method is first presented based on the minimum error Bayesian decision rule using a training picture selection method with scene change detection. Next, a joint online and offline learning method is presented, which additionally trains the loss of decision making of the proposed method based on the minimum risk Bayesian decision rule. The proposed method is implemented on HEVC test software 15.0. Experimental results show that the proposed method reduces the computational complexity of HEVC encoder to 53.6% on the average with 0.71% acceptable BD rate loss in random access configuration. For other configurations, 48.4%, 48.5%, and 54.2% encoding time savings are obtained on the average for low delay, low delay-P, and all intra configurations, respectively.
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