An automatic structure-aware image extrapolation applied to error concealment

Authors:
Haricharan Lakshman;Patrick Ndjiki-Nya;Martin Köppel;Dimitar Doshkov;Thomas Wiegand
Affiliations:
Image Processing Department, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany;Image Processing Department, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany;Image Processing Department, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany;Image Processing Department, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany;Image Processing Department, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany and Department of Telecommunication Systems, Technical University of Berlin, Ber ...
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
Cited 0

Computational Framework for Segmentation and Grouping

Computational Framework for Segmentation and Grouping
Texture optimization for example-based synthesis

ACM SIGGRAPH 2005 Papers
Is There any Texture in the Image?

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Automatic Structure-Aware Inpainting for Complex Image Content

ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Structure and texture filling-in of missing image blocks in wireless transmission and compression applications

IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel framework for spatially estimating unknown image data is presented. Common applications include inpainting, concealment of transmission errors, prediction in video coding, etc. Firstly, a segmentation of the spatial neighborhood of the area to be estimated is performed and a plausible set of segments that cross the unknown area is identified. Then, a reconstruction algorithm is developed by combining sparse modeling and patch-based synthesis. The improved extrapolation capabilities of the presented approach is shown for variety of image characteristics and the robustness of the algorithm is illustrated for large unknown blocks, which are becoming especially important for future video coding standards in order to efficiently code high resolution content.