Research On The Application Of Fractal Theory In The Encoding And Processing Of Video Surveillance Images

By means of the application of fractal theory in the encoding and processing of video surveillance image, encoding velocity and recognition results could be improved greatly. A video surveillance image can be represented by a 3-D surface of the colour intensity of each pixel position. The spatial distribution of the surface reflects the feature of image texture and it usually appears to be a fractal. Fractal dimension show the complex extent of image texture by linking the space information and colour information of images. For different regions in an image, counting-box dimension (CBD) or Brownian-motion dimension (BMD) can be used to describe quantitatively the feature of a region texture. It shows that the more complex of the image texture, the larger the fractal dimension; otherwise the simpler of the image texture, the smaller the fractal dimension. In addition, the similar image textures have almost the same fractal dimensions. Accordingly a new block matching algorithm was suggested that is concerning to Brownian-motion dimension, and furthermore region segmentation was introduced in order to propose an efficient fractal encoding algorithm. The encoding result of some standard images and video images according to this efficient fractal encoding algorithm was discussed and it is proved that this algorithm is effectiveness. A conception of fractal dimension image was proposed for the process of video surveillance image, which is obtained by sliding a window with special size on the image and calculating the fractal dimension of the image in the sliding window. Fractal dimension image is helpful to further image process and analysis such as target recognition, edge detection, and image enhancement and so on.