This paper proposes a very fast block-matching motion estimation algorithm for video compression. This method uses a new concept involving a very compact center-biased characteristic in developing directional asymmetric search patterns, which we refer to asdirectionalasymmetricsearch(DAS). The initial pattern of the DAS is a compact cross pattern containing only five initial search points. The DAS utilizes error information (block distortions) of the search patterns to determine the search direction, and then asymmetric search patterns are used in the subsequent steps accordingly. Furthermore, a prediction scheme and a best match prejudgment scheme are incorporated to favor fast motion and to benefit stationary and quasi-stationary blocks, respectively. Therefore, the proposed method significantly reduces the number of search points for locating a motion vector. Compared to conventional fast algorithms, the proposed method has the fastest search speed and most satisfactory PSNR values for all test sequences.

In this paper, we propose a novel unsupervised algorithm for the segmentation of salient regions in color images. There are three phases in this algorithm. In the first phase, we use nonparametric density estimation to extract candidates of dominant colors in an image, which are then used for the quantization of the image. The label map of the quantized image forms initial regions of segmentation. In the second phase, we define salient region with two properties; i.e., conspicuous; compact and complete. According to the definition, two new parameters are proposed. One is called ldquoImportance indexrdquo, which is used to measure the importance of a region, and the other is called ldquoMerging likelihoodrdquo, which is utilized to measure the suitability of region merging. Initial regions are merged based on the two new parameters. In the third phase, a similarity check is performed to further merge the surviving regions. Experimental results show that the proposed method achieves excellent segmentation performance for most of our test images. In addition, the computation is very efficient.