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