Motion estimation is an important and intensive task in video coding applications. Since the complexity of integer pixel search has been greatly reduced by the numerous fast ME algorithm, the computation overhead requ...Motion estimation is an important and intensive task in video coding applications. Since the complexity of integer pixel search has been greatly reduced by the numerous fast ME algorithm, the computation overhead required by fractional pixel ME has become relatively significant. To reduce the complexity of the fractional pixel ME algorithm, a directionality-based fractional pixel ME algorithm is proposed. The proposed algorithm efficiently explores the neighborhood positions which with high probability to be the best matching around the minimum one and skips over other unlikely ones. Thus, the proposed algorithm can complete the search by examining only 3 points on appropriate condition instead of 17 search points in the search algorithm of reference software. The simulation results show that the proposed algorithm successfully optimizes the fractional-pixel motion search on both half and quarter-pixel accuracy and improves the processing speed with low PSNR penalty.展开更多
In this paper, we proposed a novel Two-layer Motion Estimation(TME) which searches motion vectors on two layers with partial distortion measures in order to reduce the overwhelming computational complexity of Motion E...In this paper, we proposed a novel Two-layer Motion Estimation(TME) which searches motion vectors on two layers with partial distortion measures in order to reduce the overwhelming computational complexity of Motion Estimation(ME) in video coding. A layer is an image which is derived from the reference frame such that the sum of a block of pixels in the reference frame determines the point of a layer. It has been noticed on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame. The proposed TME performs a coarse search on the first layer to identify the small region in which the best candidate block is likely to be positioned and then perform local refined search on the next layer to pick the best candidate block in the located small area. The key feature of TME is its flexibility of mixing with any fast search algorithm. Experimental results on a wide variety of video sequences show that the proposed algorithm has achieved both fast speed and good motion prediction quality when compared to well known as well as the state-of-the-art fast block matching algorithms.展开更多
文摘Motion estimation is an important and intensive task in video coding applications. Since the complexity of integer pixel search has been greatly reduced by the numerous fast ME algorithm, the computation overhead required by fractional pixel ME has become relatively significant. To reduce the complexity of the fractional pixel ME algorithm, a directionality-based fractional pixel ME algorithm is proposed. The proposed algorithm efficiently explores the neighborhood positions which with high probability to be the best matching around the minimum one and skips over other unlikely ones. Thus, the proposed algorithm can complete the search by examining only 3 points on appropriate condition instead of 17 search points in the search algorithm of reference software. The simulation results show that the proposed algorithm successfully optimizes the fractional-pixel motion search on both half and quarter-pixel accuracy and improves the processing speed with low PSNR penalty.
文摘In this paper, we proposed a novel Two-layer Motion Estimation(TME) which searches motion vectors on two layers with partial distortion measures in order to reduce the overwhelming computational complexity of Motion Estimation(ME) in video coding. A layer is an image which is derived from the reference frame such that the sum of a block of pixels in the reference frame determines the point of a layer. It has been noticed on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame. The proposed TME performs a coarse search on the first layer to identify the small region in which the best candidate block is likely to be positioned and then perform local refined search on the next layer to pick the best candidate block in the located small area. The key feature of TME is its flexibility of mixing with any fast search algorithm. Experimental results on a wide variety of video sequences show that the proposed algorithm has achieved both fast speed and good motion prediction quality when compared to well known as well as the state-of-the-art fast block matching algorithms.
