Joint mesh-texture optimization for progressive transmission

Sheng Yang; Meiyin Shen; Chung-Chieh Jay Kuo

doi:10.1117/12.529766

18 January 2004 Joint mesh-texture optimization for progressive transmission

Sheng Yang, Meiyin Shen, Chung-Chieh Jay Kuo

Proceedings Volume 5308, Visual Communications and Image Processing 2004; (2004) https://doi.org/10.1117/12.529766
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Progressive coding of 3D textured graphic models using a joint mesh-texture optimization technique is investigated in this work. The mesh and texture data of a model are first fed into their respective compression modules to result in a series of levels of details. Then, for a given viewpoint, a rate-distortion surface is generated using these mesh and texture data. Afterwards, an optimal path over the rate-distortion surface is determined by the steepest descent algorithm. To achieve progressive transmission, the mesh and texture are transmitted in a certain ratio along the optimal path to provide the best visual quality for the given viewpoint. For further generalization, a layered sampling algorithm is proposed to deal with an arbitrary viewing angle. The performance of the proposed joint mesh-texture progressive coding algorithm is demonstrated by experimental results.

Citation Download Citation

Sheng Yang, Meiyin Shen, and Chung-Chieh Jay Kuo "Joint mesh-texture optimization for progressive transmission", Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); https://doi.org/10.1117/12.529766

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