Regular Articles

Spatiotemporal video deinterlacing using control grid interpolation

[+] Author Affiliations
Ragav Venkatesan

Arizona State University, School of Computing Informatics and Decision Systems Engineering, Tempe, Arizona 85281-8809, United States

Arizona State University, School of Electrical, Computer and Energy Engineering, Tempe, Arizona 85281-5706, United States

Christine M. Zwart

Arizona State University, School of Biological and Health Systems Engineering, Tempe, Arizona 85281-9709, United States

David H. Frakes

Arizona State University, School of Electrical, Computer and Energy Engineering, Tempe, Arizona 85281-5706, United States

Arizona State University, School of Biological and Health Systems Engineering, Tempe, Arizona 85281-9709, United States

Baoxin Li

Arizona State University, School of Computing Informatics and Decision Systems Engineering, Tempe, Arizona 85281-8809, United States

J. Electron. Imaging. 24(2), 023022 (Mar 30, 2015). doi:10.1117/1.JEI.24.2.023022
History: Received September 3, 2014; Accepted March 5, 2015
Text Size: A A A

Abstract.  With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatiotemporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance.

Figures in this Article
© 2015 SPIE and IS&T

Topics

Video ; Algorithms

Citation

Ragav Venkatesan ; Christine M. Zwart ; David H. Frakes and Baoxin Li
"Spatiotemporal video deinterlacing using control grid interpolation", J. Electron. Imaging. 24(2), 023022 (Mar 30, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.2.023022


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Journal Articles

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.