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.

The rate-constrained (R-D) motion estimation techniques have been presented to improve the conventional block-matching algorithm by using a joint rate and distortion criterion. This paper presents two motion estimation algorithms using Kalman filter to further enhance the performance of the conventional R-D motion estimation at a relative low computational cost. The Kalman filter exploits the correlation of block motion to achieve higher precision of motion estimation and compensation. In the first algorithm, the Kalman filter is utilized as a postprocessing to raise the motion compensation accuracy of the conventional R-D motion estimation. In the second algorithm, the Kalman filter is embedded into the optimization process of R-D motion estimation by defining a new R-D criterion. It further improves the rate-distortion performance significantly.

An unsupervised texture segmentation method is presented in this paper. In this paper, we propose a simple color quantization scheme to reduce the color space, and then local binary pattern (LBP) and color histogram (CH) are applied to measure the similarity of adjacent texture regions during the segmentation process. In addition, for improving the segmentation accuracy, an efficient boundary checking algorithm is proposed. The execution time of pixelwise modification is also reduced by the proposed approach. The proposed method achieves not only saving processing time but also segmenting the distinct texture regions correctly.

Video segmentation, which identifies an object from an image sequence, is a crucial issue for object-based video processing. This paper presents a mesh-based silhouette segmentation technique for video sequences. We first define an active motion activity as a temporal measure, and then a new maximum a-posteriori (MAP) scheme for spatial segmentation is developed. We also introduce clique potentials for the Gibbs distribution such that the edge connectivity and the smoothness of the homogenous patches are guaranteed. Simulation results indicate that the new method achieves very good segmentation results with a much lower computational cost, as compared to existing techniques.

The block-matching algorithm (BMA) is the most popular motion compensation technique in video coding. However, it may not provide acceptable quality at very low bit rate. Recently, the mesh-based motion compensation (MBMC) has received wide attention. It represents more general types of motion such as deformation, rotation and zooming, thus it provides better performance than BMA. This paper presents a simple but effective MBMC method that offers better objective and subjective quality than the conventional BMA and other mesh-based techniques.

Recently, the weighted least square (WLS) technique for the FIR filter design has received wide attention, since it is computationally more efficient than other minimax techniques. However, for two-dimensional (2-D) filter design, the conventional WLS technique rearranges the frequency samples and the impulse filter coefficients with 2-D form into 1-D form, thus the WLS technique results in expensive computation. This paper presents a new 2-D FIR filter design method which retains the frequency samples and impulse filter coefficients in their 2-D form. Experimental results show that the new technique is computationally very efficient and leads to nearly-optimal approximations.