In High Efficiency Video Coding,the Residual Quad-Tree(RQT) coding is used to encode the prediction residual for both intra and inter Coding Units(CU) and provides improved coding gains. However, this results in much ...In High Efficiency Video Coding,the Residual Quad-Tree(RQT) coding is used to encode the prediction residual for both intra and inter Coding Units(CU) and provides improved coding gains. However, this results in much higher computational complexities.To address this problem, we develop two fast RQT algorithms for intra- and inter-prediction residual coding respectively. For intra coding,the proposed algorithm selects the best prediction mode in the rate distortion mode decision process using a Prediction Unit(PU) size-dependent fast RQT depth decision on a reduced prediction mode candidates set from the rough mode decision process. For inter coding, in addition to CU size-dependent fast RQT depth decisions, we propose a discriminant analysis-based fast depth decision algorithm to determine the best transform unit size. Experimental results show that on average, we can realise a 21.29% encoding time saving and 0.03%bit-rate reduction for intra coding, while 15%of the encoding time can be saved with a negligible coding performance loss for inter coding.展开更多
The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we propose...The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61272502the National Basic Research Program of China (973 Program) under Grant No.2010CB327900the National Science Fund for Distinguished Young Scholars under Grant No.61125206
文摘In High Efficiency Video Coding,the Residual Quad-Tree(RQT) coding is used to encode the prediction residual for both intra and inter Coding Units(CU) and provides improved coding gains. However, this results in much higher computational complexities.To address this problem, we develop two fast RQT algorithms for intra- and inter-prediction residual coding respectively. For intra coding,the proposed algorithm selects the best prediction mode in the rate distortion mode decision process using a Prediction Unit(PU) size-dependent fast RQT depth decision on a reduced prediction mode candidates set from the rough mode decision process. For inter coding, in addition to CU size-dependent fast RQT depth decisions, we propose a discriminant analysis-based fast depth decision algorithm to determine the best transform unit size. Experimental results show that on average, we can realise a 21.29% encoding time saving and 0.03%bit-rate reduction for intra coding, while 15%of the encoding time can be saved with a negligible coding performance loss for inter coding.
基金co-supported by the National Natural Science Foundation of China (Grant Nos. 61371134, 61071137)the National Basic Research Program of China (No. 2010CB327900)
文摘The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.
