Block-matching motion estimation algorithms for video processing and compression: A brief overview

Humanity created different methods for sharing information. One of the first forms of sharing information and knowledge were images. In the beginning, the process of sharing was relying on static appearances. With the invention of moving pictures by Eadweard Muybridge in the first part of 1870s, this exchange and sharing gained a new quality. Now it was possible to show and preserve motion too. Since that time, technology has changed rapidly. The latest discoveries and improvements from the point of view of technology use computer and IT technologies extensively. Today it is possible for everybody to create and record movies by themselves using affordable and convenient technological devices. Also the process of sharing evolved rapidly and become cheaper and cheaper. We are now able to record some movies and share them through the Internet or other carriers in real time or near real time. However, this also creates serious problems due to the huge volume of data to be sent through the data lines. Therefore, research has concentrated on methods to decrease the data volume without losing the quality. One way to do that is to create effective CODECs. A major drawback of moving pictures is the motion itself. CODECs have to minimize the size of videos without paying the price of quality losses but have also to reduce the computational complexity. Both of these requirements can be achieved with a solid knowledge of motion estimation among others. This paper gives a general overview and survey of some existing and important approaches without the claim of having a complete overview of the field

BLOCK MOTION ESTIMATION USING DIRECTIONAL ADAPTIVE SEARCH WINDOW

Motion estimation (ME) is the exploitation of similarities between adjacent frames in a video sequence by eliminating temporal redundancy, and is an essential part of the H.264 and other video compression standards. However, it introduces an increase of computation complexity resulting in longer execution time. Thus, adaptive motion estimation for H.264 is proposed in order to reduce the execution time while giving better PSNR performance. The algorithm determines the amount of motion in each block and classifies them as low, medium and high motion. From the magnitude and direction of the x andy motion vector components, the search window (search range) is dynamically adjusted. For high motion, the search range is set to be the maximum value and vice versa. The results show that execution time could be reduced to almost half (50%) of the conventional method since the number of search points and computations decrease inthe range of40% to 60%. Furthermore, the method gives a better image quality for video sequence with uniform motion and negligible PSNR loss in others. By introducing early termination inthe adaptive motion estimation, the number of computation could be reduced even further since the search process is terminated immediately certain criteria are satisfied. By using Option 2 for early termination, the search point computation and PSNR is reduced with average 1.3% and 1.027% from the adaptive motion estimation without the early termination process