Homogeneous but distinct visual objects having low-contrast boundaries are usually merged in most of the segmentation algorithms. To alleviate this problem, an efficient image segmentation algorithm based on a bottom-up approach is proposed by using spatial domain information only. For initial image segmentation, we adopt a new marker extraction algorithm conforming to the human visual system. It generates dense markers in visually complex areas and sparse markers in visually homogeneous areas. Then, two region-merging algorithms are successively applied so that homogeneous visual objects can be represented as simple as possible without destroying low-contrast real boundaries among them. The first one is to remove insignificant regions in a proper merging order. And the second one merges only homogeneous regions, based on ternary region classification. The resultant segmentation describes homogeneous visual objects with few regions while preserving semantic object shapes well. Finally, a size-based region decision procedure may be applied to represent complex visual objects simpler, if their precise semantic contents are not necessary. Experimental results show that the proposed image segmentation algorithm represents homogeneous visual objects with a few regions and describes complex visual objects with a marginal number of regions with well-preserved semantic object shapes.

In this paper, we propose a fast multi-resolution block matching algorithm with three resolution levels (upper, middle, and lower levels) for multiple-frame motion estimation (MFME). The main concept of the algorithm is to perform a fast search while maintaining a PSNR performance similar to a full search block matching algorithm (FSBMA). The algorithm combines motion vector prediction using the spatial correlation of motion vectors and a multiple candidate search based on a multi-resolution search. To further reduce the computational complexity, we propose two temporal reduction schemes. To reduce the number of previous reference frames to be processed, the first scheme is applied to the upper level by using the information obtained from the search results of the spatio-temporally adjacent macroblocks (MBs) and the result from the current MB in the middle level of the first reference frame. The other scheme is applied to the lower level by using statistical information. Experimental results show that the proposed algorithm guarantees an average PSNR loss of less than 0.23 dB with dramatically reduced computational complexity as compared to the FSBMA. In particular, for sequences with fast motion or frame skipping, the proposed method provides a more prominent PSNR performance than those of existing fast schemes with a comparable computational complexity.

A region-based adaptive perceptual quantization technique is proposed for video sequence coding, and applied to the MPEG coder. The visibility of coding artifacts in a macroblock (MB) is affected by perceptual characteristics of neighboring MBs as well as the MB itself. Therefore spacial and temporal activities of the MB and its surroundings are used to decide the quantization scaling factor. In comparison with the adaptive scheme in the encoding algorithm specified in MPEG-2 Test Model 5 (TM5), the proposed scheme is proven to improve perceptual quality further in video coding.