IEEE
Transactions on Image Processing, vol. 11, no. 2, Feb.
2003
Foveation Scalable Video
Coding with Automatic Fixation Selection
Zhou
Wang1, Ligang Lu2,
and Alan C. Bovik1
1 Laboratory for Image and Video Engineering
(LIVE), Department of Electrical and Computer Engineering, The University of
Texas at Austin, Austin, TX 78712-1084
2 IBM T. J. Watson Research Center, Yorktown
Heights, NY 10598
Abstract: Image and video coding is an optimization
problem. A successful image and video coding algorithm delivers a good tradeoff
between visual quality and other coding performance measures, such as
compression, complexity, scalability, robustness, and security. In this paper,
we follow two recent trends in image and video coding research. One is to
incorporate human visual system (HVS) models to improve the current
state-of-the-art of image and video coding algorithms by better exploiting the
properties of the intended receiver. The other is to design rate scalable image
and video codecs, which allow the extraction of coded visual information at
continuously varying bit rates from a single compressed bitstream.
Specifically,
we propose a foveation scalable video coding (FSVC) algorithm which supplies
good quality-compression performance as well as effective rate scalability. The
key idea is to organize the encoded bitstream to provide the best decoded video
at an arbitrary bit rate in terms of foveated visual quality measurement. A
foveation-based HVS model plays an important role in the algorithm. The
algorithm is adaptable to different applications, such as knowledge-based video
coding and video communications over time-varying, multi-user and interactive
networks.
Index
Terms
– video coding, rate scalable coding, human visual system, foveation, image and
video quality, wavelet