Jump to first page The research report Block matching algorithm Motion compensation Spatial transformation Xiaomei Yu

Jump to first page Motion compensation: n Motion compensation methods can be defined as techniques that divide images into local regions (block or patches) and estimate a set of motion paramaters for each region. n The procedure is to synthesizes the predicted image of the nth frame from the decoded image of the previous frame can be regarded as an image warping or texture mapping process. It can be written as where the geometic relationship between and is defined by the transformation funcations and.

Jump to first page Motion compensation: n Block matching algorithm (BMA) estimates the motion of blockes and transmits the estimated motion vectors. BMA can be regarded as a method that applies spaial transformations on the blocks of the image. n Pel-recursive algorithm (PRA) estimates the motion of each pixel and does not transmit the motion information.

Jump to first page Block matching algorithm n BMA adopts a motionmodel that describes the motion of the image objects by the translational motion of blocks. The problems of BMA (e.g. blocking artifacts) are caused by the insufficiency of the motion model to represent the real world. To overcome this defect, many coding methods have been proposed. n BMA can be regarded as a method that applies spatial transformations on the blocks of the image. By generalizing this transformation, new motion compensation methods that adopt different motion models can be developed.

Jump to first page n For example in BMA, the transformation functions for the pixels in the i th block of the image are n Various motion compensation methods can be composed by adopting different transformation functions. Transformation function: Approximate the original motion vector field woth a smaller number of parameters A smaller amount of computation.

Jump to first page n The total amount of computation in texture mapping is a sum of the following three processes: (a) computation of the motion parameters (b) computation of the transformation function ( c) interpolation of intensity values. The later two are important since they are performed each pixel. The computational cost of (b) depend on the existence of an effective scan line algorithm, which scans the image computing f(x, y) and g(x, y) for each pixel.

Jump to first page Spatial transformation n Affine transformation (AFMC) n Bilinear transformation (BLMC) n Perspective transformation

Jump to first page The advantages/disadvantages n The parameters can be determined from motion vectors of three vertices. Then, the patches of this transformation are triangles. The cost in computing the motion parameters remains cheap regardless of the shape of the patch. An effective scan line algorithm exists. n Only rectangular patches are allowable since otherwise the computation of the motion parameters become complicated. An effective scan line algorithm with two additions per pixel exists for this transformation. n The scan line algorithm requires two divisions for each pixel.