For mobile visual communications, the development of more robust and efficient video traffic control and transmission techniques remains one of the most important issues. Foveated video originates from visual entropy reduction by removing undetectable high visual frequencies that occur at a distance from the fixation point. In this paper, compression gain is defined and measured to quantify the enhanced performance when the visual throughput of the regions of interest (ROI) is increased over a capacity-limited channel.

For point-to-point mobile visual communications, layered video has been utilized to adapt to time-varying channel capacity over noisy environments. From the perspective of the HVS (Human Visual System), it is necessary to minimize the loss of visual quality by specifically maintaining the throughput of visually important regions, objects and so on. Utilizing the prioritized bitstreams generated according to each layer, the throughput can be improved for given channel statistics. In this paper, we define the transmission gain and measure the improved performance when the throughput of ROI (Regions Of Interest) is increased relative to visually unimportant regions over a capacity limited mobile channel.

This paper presents a framework for automatic video region-of-interest determination based on visual attention model. We view this work as a preliminary step towards the solution of high-level semantic video analysis. Facing such a challenging issue, in this work, a set of attempts on using video attention features and knowledge of computational media aesthetics are made. The three types of visual attention features we used are intensity, color, and motion. Referring to aesthetic principles, these features are combined according to camera motion types on the basis of a new proposed video analysis unit, frame-segment. We conduct subjective experiments on several kinds of video data and demonstrate the effectiveness of the proposed framework.

This paper proposes a new method of retrieving images from large image databases. The method is based on VQ (Vector Quantization) of local texture features at interest points automatically detected in an image. The texture features are extracted by Gabor wavelet filter bank, and rearranged for rotation. These features are classified by VQ and then construct a pattern histogram. Retrievals are performed by just comparing pattern histograms between images.

We describe an index for estimating the level of interest in Web pages. This "time-based interest" (TBI) index combinates an equation reflecting page accesses and an equation reflecting the decrease in interest over time. These equations work simultaneously by using a parameter that is based on the time since the last access. We experimentally estimated the decrease ratio of the TBI index and evaluated the characteristics of the TBI equation. We found that the index follows Zipf's distribution, indicating that reflects the change in popularity. We also introduce an access-log analysis system called CyberRanking that includes TBI analysis. CyberRanking analyzes the access logs of Web servers and presents the results in 2-D or 3-D graph on a Web browser.

In the circumstances we want to deal with, transmission channel is limited and global motion can happen by camera movement, and also there exists a region-of-interest (ROI) which is more important than background. So very low bit rate coding algorithm is required and processing of global motion must be considered. Also ROI must be reconstructed with required quality after decoding because of its importance. But the existing methods such as H. 261, H. 263 are not suitable for such situations because they do not compensate global motion, which needs large amount of transmission bits in motion information and degrades image quality. And also they can not reconstruct ROI's with high quality because they do not consider the fact that ROI's are more important than background. So a new coding scheme is proposed that describes a method for encoding image sequences distinguishing bits between ROI and background. Simulations show that the suggested algorithm performs well especially in the circumstances where background changes and the area of ROI is small enough compared with that of background.