Predictive video decoding using GME and motion reliability

Yue-Meng Chen; Ivan V. Bajić

doi:10.1117/12.892379

24 September 2011 Predictive video decoding using GME and motion reliability

Yue-Meng Chen, Ivan V. Bajić

Proceedings Volume 8135, Applications of Digital Image Processing XXXIV; 81350O (2011) https://doi.org/10.1117/12.892379
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

In this paper, we present an improved approach to predictive video decoding based on global and local motion reliability. The framework consists of three processing stages. Global motion (GM) estimation and motion reliability analysis are the key components in the first stage, where we model global motion and refine the MV field. In the second stage, we predict local and global motion for the target frame, and determine corresponding weights based on the Linear Minimum Mean Square Error (LMMSE) criterion. Finally in the third stage, a temporal interpolator is applied to compose two future frames, which are linearly combined to form the final predicted frame. Our results indicate the proposed method achieves better visual quality compare to other state-of-the-art predictive decoding approaches, particularly in sequences involving moving camera and objects.

Citation Download Citation

Yue-Meng Chen and Ivan V. Bajić "Predictive video decoding using GME and motion reliability", Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81350O (24 September 2011); https://doi.org/10.1117/12.892379

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available