Luc Van Eycken

Motion-compensated interframe prediction

Motioncompensated interframe prediction has been applied widely in digital television signal coding specially in low bit-rate coding such as videotelephone and videoconference. A key element in motion-compensated interframe prediction is the motion estimation algorithm. In this paper a new motion estimation algorithm is presented. It is based on the block recursive (gradient) method and makes use of some of the advantages of the blockmatching method. Motion estimation with non-integer pd accuracy can be obtained with only a few iterations. In addition some techniques with respect to motion correlation and motion tendency estimation which we proposed before [1] are applied to the estimation algorithm which are efficient enough to improve the performance of the motion estimation and the homogeneity of the estimated motion vector field. The experiment shows that the proposed algorithm has a much higher estimation accuracy and a much better prediction performance than the conventional block motion estimation algorithms. Moreover a coding approach designed effectively to minimize the bit-rate necessary to present the motion vector field is proposed as well. A high compression rate for the transmission of the motion information has been achieved. Finally the simulation results of motioncompensated interframe prediction for low bit-rate coding based on the proposed algorithm and of the motion information encoding are presented.

Image sequence coding using data classification

In this paper a specific data classification approach is presented which can be used in coders for transmitting video sequences. The image data is locally classified into four categories prior to the coding process which is then divided in four modes in order to exploit the masking effect of motion and to reach high performance. The four categories are: moving area containing an edge (data class Cf) moving area containing no single edge (class C/j) still area containing an edge (class C) and still area containing no single edge (class CE). This classification approach is based on the fact that the human visual system is especially sensitive to edge information and this in function of the motion behavior. Generally it is known that the edge information is critical to the human eyes. In the moving areas however the human eyes are less critical of the spatial details in comparison to that in the still areas because of the masking effect of motion. The data in the moving areas can be treated relatively coarsely but when edges are observed the quality criterion should still be raised appropriately. Using this four-mode coding method the subjective reproduction quality can be improved even in the case of low bit-rate applications. This classification approach is applied in the conventional motion compensating hybrid (DCT/DPCM) coder although it can also be with other coders.

Improved motion estimation algorithm with post processing and its application in motion-compensated interpolation

Motion-compensated interpolation has several applications in digital image processing like field frequency conversion of different television standards, image coding with the frame skipping method, and so on. Differing from a motion-compensated (MC) predictive algorithm which aims to minimize the prediction errors that must be coded and transmitted, the motion estimator for MC interpolation must provide reliable motion vectorfields which closely approximate the actual motions in scenes in order to reconstruct the missed images in the receiver end. Generally speaking, motion vectorfields obtained by the conventional motion estimation algorithms are not good enough for the application of MC interpolation. In this paper, a new motion estimation algorithm including smoothness constraints is proposed, and its application to MC interpolation of skipped images in the environment of a low bit-rate codec is investigated. The simulation results show that motion vectorfields obtained by the proposed motion estimation algorithm are more homogeneous and more reliable than those obtained by the conventional algorithms, and the quality of interpolated images is substantially improved for the examined sequence.

High Speed Robot Vision Using Specialized Hardware

Because of the required speed in robot vision applications, it seems obvious to speed up some time consuming phases in the image processing with special hardware. The preprocessing stage is likely to be the most suitable for hardware implementation because here the amount of data to be processed is the largest and because the computations are not too complex. This paper will discuss a few hardware building blocks for image preprocessing that have proved to be very useful, such as the computation of the low order moments of objects in binary images, runlength encoding, projections and profiles, gradient operators for edge detection.