Texture image compression and restoration for 3D visualization of urban buildings

Yucai Xue; Zuxun Zhang; Jianqing Zhang

doi:10.1117/12.441601

25 September 2001 Texture image compression and restoration for 3D visualization of urban buildings

Yucai Xue, Zuxun Zhang, Jianqing Zhang

Proceedings Volume 4553, Visualization and Optimization Techniques; (2001) https://doi.org/10.1117/12.441601
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

The target of the research is the image compression and restoration of the texture surface of the buildings for 3-D visualization of cities in this paper. A new method based on texture segmentation and the texture's self-similitude is introduced. The character of the method is that it can remove the interference from the source image, which was obtained in the nature environment, and realize image compression in the same time. The method takes advantage of achieving higher compression ratio than other conventional compression methods, and the algorithm is fast enough to satisfy the 3D reconstruction's need. The restoration image's vision effect is true to nature as a whole. The method is more suitable for the wall texture image compression and restoration of 3D visualization in cities than other traditional compression methods.

Citation Download Citation

Yucai Xue, Zuxun Zhang, and Jianqing Zhang "Texture image compression and restoration for 3D visualization of urban buildings", Proc. SPIE 4553, Visualization and Optimization Techniques, (25 September 2001); https://doi.org/10.1117/12.441601

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