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This paper presents a new fast motion estimation method where search ranges are determined by the probabilities of motion vector differences (MVDs). The MVDs' distribution is investigated and its parameter is estimated by the maximum likelihood estimator. With the estimated distribution, the search ranges are efficiently restricted by a prefixed probability for MVDs. Experimental results show that the performance of the proposed method is very similar to that of the full search algorithm in PSNR but it enables significant reduction in the computational complexity.

We propose a rate control scheme using a rate-distortion (R-D) estimation model, which produces a consistent picture quality between consecutive frames. Our R-D estimation method offers a closed-form mathematical model that enables us to predict the bits and the distortion generated from a frame encoded at a given quantization parameter (QP) and vice versa. Its most attractive feature is its low computational complexity. Furthermore, it is accurate enough to be applied to practical video coding. In our simulation, the estimation errors for rate and distortion are less than 2.5% and 1.5%, respectively. Therefore, the proposed rate control scheme is appropriate for applications requiring low delay, low complexity, and the ability to control output bit-rate and quality accurately. Our scheme ensures that the video buffers do not underflow or overflow by satisfying the buffer constraint, and it also prevents quality difference between consecutive frames from exceeding a certain demanded level by adopting a distortion constraint. In addition, a consistent picture quality is maintained within a frame, and error propagation, caused by quality degradation of anchor frames, is reduced by differentiating the control procedure for anchor frames from that for nonanchor frames. Simulation results show that our control scheme achieves 0.52-1.84 dB peak signal-to-noise ratio (PSNR) gain over MPEG-2 Test Model 5 (TM5) rate control and maintains very consistent quality within a frame as well as between frames.

This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates luminance and motion information simultaneously and uses morphological tools such as morphological filters and watershed algorithm. The procedure toward complete segmentation consists of three steps: joint marker extraction, boundary decision, and motion-based region fusion. First, the joint marker extraction identifies the presence of homogeneous regions in both motion and luminance, where a simple joint marker extraction technique is proposed. Second, the spatio-temporal boundaries are decided by the watershed algorithm. For this purpose, a new joint similarity measure is proposed. Finally, an elimination of redundant regions is done using motion-based region fusion. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstrates the efficiency of the proposed method.