Advances in region-based texture modeling for video compression

Fan Zhang; David R. Bull

doi:10.1117/12.896314

24 September 2011 Advances in region-based texture modeling for video compression

Fan Zhang, David R. Bull

Proceedings Volume 8135, Applications of Digital Image Processing XXXIV; 81350N (2011) https://doi.org/10.1117/12.896314
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

This paper presents a parametric video compression framework which exploits both texture warping and dynamic texture synthesis. A perspective motion model is employed to warp static textures and a dynamic texture model is used to synthesise time-varying textures. An artefact-based video quality metric (AVM) is proposed which prevents spatial and temporal artefacts and assesses the reconstructed video quality. This is validated using both the VQEG database and subjective assessment, and shows competitive performance on both non-synthetic and synthetic video content. Moreover, a local Rate-Quality Optimisation (RQO) strategy is developed based on AVM in order to make a decision between waveform coding and texture warping/synthesis. The proposed method has been integrated into an H.264 video coding framework with results offering significant bitrate savings for similar visual quality (based on both AVM and subjective scores).

Citation Download Citation

Fan Zhang and David R. Bull "Advances in region-based texture modeling for video compression", Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81350N (24 September 2011); https://doi.org/10.1117/12.896314

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