In this paper, a new method capable of effectively coding arbitrarity-shaped image regions is presented. The image region is spanned into the 8 8 rectangular block and its intermediate luminances are interpolated. After all liminances in the 8 8 block are obtained from pixels in the region, they are transformed by 8 8 DCT. The proposed extension/interpolation (EL) method is compared with conventional ones, such as SA-DCT, mean stuffing, etc., under three aspects: peak signal-to-noise ratio (PSNR), hardware complexity, and the flexibility for improvement of performance. Simulation results show that the performance of the proposed method is superior to that of the conventional ones. In addition, we introduce an improved version by repetitively performing the EL method.

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.

This paper describes a method of coding image sequences based on global/local motion information. The suggested method initially estimates global motion parameters and segments a target region from a given image. Then we coded background and target region by assigning more bits to the target region and less bits to background in order to reconstruct the target region with high quality. Simulations show that the suggested algorithm has better result than the existing methods, especially in the circumstances where background changes and target region is small enough compared with that of background.

A new motion field segmentation algorithm under the 8-parameters motion model is presented which uses a multipass iterative region-refining techinique. The iterative region-refining module consists of a seed block detection and subsequent region-refining iterations. An initial estimate of an object motion is provided in the seed block detection process. This initial estimate is iteratively updated and approaches to a reliable mapping parameter set in region-refining process. A multipass composition of the module makes it possible to detect multiple motions in a scene. Our simulation results confirm that the proposed method successfully partitions an image into independently moving objects with allowable computation time.

In this paper, we propose an analytic method for dimensioning traffic descriptors at the leaky bucket-based UPC for VBR MPEG video traffic on ATM networks. We analytically derived cell violation probabilities at the UPC by using a proposed scene-based video traffic model, and then we showed that it was possible to select sets of traffic descriptors that produce the required violation probability. In two example video traces, the numerical results showed that our proposed traffic descriptor dimensioning method well approximated the simulation-based traffic control results of the real video traces. In cases where an effective bandwidth allocation method based on the ON/OFF model was used for the call admission control in the networks, we compared the allocated effective bandwidth to each set of traffic descriptors that produced zero UPC losses.

Unlike a noise removal recursive or averaging filter, this letter presents a temporal filter which attenuates temporal high frequency components and improves visual effects. Although temporal aliasing occurs, the proposed filter proceeds temporal bandlimitation not affected by them. To reduce effects caused by aliasing components, a spatial filtering which is applied along the trajectory of motion is investigated. The proposed filter presents a de-aliasing and effective bandlimiting characteristics as well as reducing of noises.

A fast Newton/LMS algorithm is proposed which uses an efficient inversion technique of input autocorrelation matrix when the periodic pseudo random sequence is used as the reference signal. The number of operations is greatly reduced and the computational results show fast convergence rate and low misadjustment error. And the application of the algorithm to the case of nonperiodic reference signal is described.

Gradient-based methods for the computation of the velocity from image sequences assume that the velocity field varies smoothly over image. This creates difficulties at regions where the image intensity changes abruptly such as the occluding contours or region boundaries. In this letter, we propose a method to overcome these difficulties by incorporating the information of discontinuities in image intensity into a standard local optimization method. The presented method is applied to the synthetic and real images. The results show that the velocity field computed by the proposed method is less blurred at region boundaries than that of the standard method.

In formulating the motion constraint equation, we implicitly take it for granted that the spatial and temporal sampling intervals are very small. In real situations, since the intervals cannot be considered sufficiently small, an error will be introduced into the constraint equation and consequently the velocity estimate will be subject to an error due to inaccuracy of the constraint equation. We perform some experiments to analyze the effect of sampling interval on motion estimation. The understanding of experimental results will provide an insight into necessity and amount of image filtering prior to the application of motion estimation.