ons. Therefore the best match motion gain. vector estimate MV(m = X, n=y) is the pixel displace- Moving foregrounds are modeled as non-rotational ment between the block [*(i,j) in frame k, and the best rigid objects that move independent of the background. matched block -k-(i+x,j+y) in the displaced frame k-l. - Moving objects can be detected by matching the fore- The best match is depicted in Fig. 7.4-6. ground object between two frames. A perfect match In cases where the block motion is not uniform or if results in zero difference between the two frames. In the scene changes, the motion estimate may in fact in- theory, moving foregrounds can also achieve additive crease the bit rate over the corresponding spatial coding gain. In practice, moving objects are subject to encoding of the block. In the case where the motion es- occlusion, rotational and non-rigid motion, and illumi- timate is not effective, the video encoder does not use nation variations that reduce the achievable coding gain. the motion estimate, and encodes the block using the Motion compensation systems that make use of motion spatial encoder. estimation methods leverage both background and fore- The search space size determines the complexity of ground coding gain. They provide pure interframe dif- the motion estimation algorithm. Full search methods ferential encoding when two backgrounds are static, i.e., are costly and are not generally implemented in real-time the computed motion vector is (0,0). And the motion video encoders. Fast searching techniques can consider- estimate computed in the case of moving foregrounds ably reduce computational complexity while maintaining generates the minimum distortion prediction. good accuracy. These algorithms reduce the search pro- Motion estimation is an interframe prediction process cess to a few sequential steps in which each subsequent falling in two general categories; pel-recursive algorithms search direction is based on the results of the current [16] and block-matching algorithms (BMA) [17]. The