Simon Teran (2008) A stochastic algorithm for block-based motion compensation in video coding. MSc thesis.
The main purpose of this work is development of a new algorithm for motion estimation. The new algorithm, which is based on learning automata, is explained and evaluated based on comparison with frequently used algorithms for motion estimation. In the first part a general approach to video compression is presented, later on specific example of standard (H.264 or MPEG-4 part 10) for video compression is explained. The video compression process in general consists of two steps. The first step is removing of the temporal redundancy. This is possible due to similarities between neighboring frames in the video sequence. In the second step spatial redundancy is removed. As it will be seen, this part is very similar to static image compression. From the first part the complete process of video compression is clearly presented. The concept of removing temporal redundancy is based on motion estimation. These facts make the decision, which algorithm to use, very important in video encoder development. Second part of this work is dedicated to motion estimation algorithms. First, the most frequently used algorithms are presented. After that, a new approach to solving motion estimation problem, which is based on learning automata, is introduced. The basic ideas of learning automata are shown and also their application in motion estimation problem. Some disadvantages of this algorithm are also shown as well as possible workarounds. At the end of this work the new algorithm is compared to other algorithms for motion estimation. All the algorithms are used on three different video sequences. They are compared regarding processing time and quality of results. It is shown that all algorithms offer approximately equal ratio between processing time and quality. The new algorithm falls in the middle if algorithms are ordered based on processing time. The main competitor in this field is popular Tree Step Search. The new algorithm produces better results than his competitor Tree Step Search.
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