Abstract
In this paper, a novel diamond search window (SW) based full search algorithm is proposed. To dynamically adjust the size of the diamond search window, a frame level adaptive search range (SR) algorithm is presented. Laplace distribution is used to model the optimal motion vector difference (MVD) distribution within a frame and the model parameters are estimated by maximum likelihood estimation (MLE). The data used for MLE is also carefully studied. With the estimated distribution model, proper SR is obtained dynamically frame by frame. Experiment results show that a diamond SW outperforms the traditional squared SW. Also, the proposed adaptive search range algorithm properly adjusts the diamond SW size. The proposed diamond SW based adaptive search range (ASR+DSW-FS) algorithm obtains the same or even better performance compared with conventional full search algorithm, while significantly reduces the computational complexity.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
