A review on fractal compression and motion estimation techniques

In many video applications fractal video compression is use for video coding caused by its different features and lower bit rate. Self similarity concepts of image compression are used in fractal video compression. However selfsimilarity means that fractal picture is consists of duplicates of itself that are interpreted and indicated by a change. More computational complexity is present in fractal video compression for reducing this complexity different technique has been implemented. In video compression, finding the motion vectors (MV) is one of the major factor in motion estimation, due to its high computation complexity allows in between the frames. Many application like multimedia service contains the temporal type of redundancies for emission of video i.e. storage space, bandwidth and transmission cost to reduces this kind of redundancy the motion estimation is used while not degrade a quality of the video. There are number of algorithm has been evolved for fast block based matching techniques in motion estimation to remonstrate the drawbacks relate to diminishing the number of searching point, complexities and computational cost etc., by reason of its effortlessness the block-based technique is demand in motion estimation. Block matching algorithms attracts many researchers from algorithms.the different domain for motion vector estimation also for solving real life applications in motion estimation for video coding. This paper laborite a review of various fractal compression techniques and block matching motion estimation purpose. So, transmission of video takes more time to reach its destination. Therefore, some video compression techniques are involved to remove the redundancy that present in original video. In continuation of fractal image compression uses fractal video compression technique. One of the image compression methods is fractal coding [1]. Its clam is that within a given local region the correlation not only presents in adjacent pixels, but also in global regions or different regions. Mainly video compression technique contains two types of technique i.e. lossy and lossless compression [2]. In lossless technique, reconstruction of total original data is possible. Due to this characteristic, most lossless compression technique referred it for data and executable files etc. But few data may be removed permanently in lossy compression. Mainly two types of redundancies are evolving in sequence of video they are temporal redundancy & spatial redundancy. Spatial redundancies define as correlation present in a frame among neighboring pixel value. Temporal redundancy means by considering a redundancy present in between adjacent frames of images in video. The interframe coding concept uses to lower the temporal redundancy. Similarly, the intraframe coding concept lower the spatial type of redundancy.