This paper proposes a transcoding technique with low computational complexity, by means of eliminating high order DCT coefficients. Although the computational complexity of the DCT coefficient reduction technique is very low, this technique induces severe drift errors and produces poor-quality blockish pictures; artifacts are
especially noticeable in interlaced video. Therefore, we propose a fast transcoding method of interlaced video which can reduce these artifacts. Our technique detects problem blocks, and allocate them with higher order DCT coefficients into the new MPEG stream. As a result of using our proposed technique, experiments show that artifacts caused by drift errors are mostly reduced, and the computational complexity is very low. Only 4.1% of all the luminance blocks are detected as problem blocks. Hence, most blocks are filled with the minimum set of DCT information required in the transcoded stream. Moreover, we develop a networked video browsing system by using our proposed transcoding method to play multiple streams in a client viewer, simultaneously.
This paper presents new algorithm for a person extraction system in video. Generally, segmentation schemes are based on some criteria related to homogeneous properties of image features, such as color and motion. However, typical semantic objects comprise multiple regions having different properties, and this severely affects the segmentation results. In this paper, we propose a method to extract the block-based boundaries of semantic objects as one of the key components of our system. The method relies on an idea of integrating the manipulations of image features at an initial level with no semantics (e.g., color) and an object model at a higher level with semantics. To do so, we use genetic programing (GP) to create the object model with a set of training images. A Maximum A Posteriori (MAP) estimation procedure is applied so that the object model and the image features are integrated. In a testing process, we fuse two segmentation results: the block-based contour extracted with the MAP procedure and arbitrary shaped regions obtained with a color segmentation scheme. Thus, the final contour of an object is acquired. The proposed algorithm is applied to extract the head and the body of a person in our experiment.
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