A Fully Adaptive Distance-Dependent Thresholding Search (FADTS) Algorithm for Performance-Management Motion Estimation

Trading off computational complexity and quality is an important performance constraint for real time application of motion estimation algorithm. Previously, the novel concept of a distance-dependent thresholding search (DTS) was introduced for performance scalable motion estimation in video coding applications. This encompassed the full search as well as other fast searching techniques, such as the three-step search, with different threshold settings providing various quality-of-service levels in terms of processing speed and predicted image quality. The main drawback of the DTS was that the threshold values had to be manually defined. In this paper, the DTS algorithm has been extended to a fast and fully adaptive DTS (FADTS), a key feature of which is the automatic adaptation of the threshold using a desired target and the content from the actual video sequence, to achieve either a guaranteed level of quality or processing complexity. Experimental results confirm the performance of the FADTS algorithm in achieving this objective by demonstrating either comparable or improved search speed over existing fast algorithms including the diamond search, hexagon-based search, and enhanced hexagon-based search, while maintaining similar error performance