By Topic

Error surface-aware modeling algorithm for quarter-pixel motion estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Junsang Cho ; Dept. of Electron. & Comput. Eng., Hanyang Univ., Seoul, South Korea ; Suh, J.W. ; Gwanggil Jeon ; Jooheung Lee
more authors

In this paper, an error surface-considered modeling algorithm for quarter-pixel motion estimation during video encoding is presented. We previously proposed two algorithms: a model-based quarter-pixel motion estimation (MBQME) algorithm and a hierarchical model-based quarter-pixel motion estimation (HMBQME) algorithm. MBQME is an interpolation-free algorithm that has a minimum motion estimation time, while HMBQME has selective interpolation according to the decision process. Consequently, the peak signal-to-noise ratio (PSNR) for HMBQME is better than that of MBQME, but the motion estimation time is also increased. As an alternative method, we propose an error surface-considered modeling algorithm. In this scheme, the tendency of the error surface is first assessed. Using the strength of the edge at the error surface, we can classify the error surface region as plain or textured. For plain regions, interpolation-free and simple-structured modeling is appropriate for the quarter-pixel motion estimation method. In this case, we modified conventional mathematical modeling algorithm suitable for plain region. For textured regions, additional interpolation is needed for more accurate modeling. We calculate the half-pixel SAD values and perform more accurate modeling so as to find the best motion vector (MV). The experimental results show that the proposed scheme has better PSNR performance than any previous algorithms with minimum motion estimation time.

Published in:

Consumer Electronics, IEEE Transactions on  (Volume:58 ,  Issue: 3 )