Abstract
In this letter, an improved three-step search algorithm is presented, which uses both gray and chromatic information to boost the performance with random optimization and converge the motion vectors to global optima. Experimental results show that this algorithm can efficiently improve the PSNR after motion compensation.
This is a preview of subscription content, log in via an institution to check access.
References
A. Murat Tekalp, Digital Video Processing, Prentice Hall PTR, 1995, chap.6.
John L. Maryak, Daniel C. Chin, Stochastic approximation for global random optimization, Proceeding of the American Control Conference, Chicago, Illinois, USA. vol.1, 2000, 3294–3298.
James C. Spall, Stacy D. Hill, David R. Stark, Some theoretical comparisons of stochastic optimization approaches, Proceeding of the American Control Conference, Chicago, Illinois, USA. vol.1, 2000, 1904–1908.
Author information
Authors and Affiliations
About this article
Cite this article
Liu, Z., Liu, T. & Zhang, X. Joint random optimized three-step search algorithm based-on gray and chromatic information. J. of Electron.(China) 19, 215–217 (2002). https://doi.org/10.1007/s11767-002-0039-6
Issue Date:
DOI: https://doi.org/10.1007/s11767-002-0039-6