In this paper, we propose an efficient requantization method for transcoding of MPEG video. Transcoding is the process of converting a compressed video format to another different compressed video format. We propose an simple and efficient transcoding by requantization in which MPEG coded video at high bit-rate is converted into MPEG bitstream at lower bit-rate. To reduce a image quality degradation, we use HVS (Human Visual System). By using this effect, the part of image in high activity region is coarsely quantized without seriously degrading the image quality. Experimental results show that the proposed method can provide good performance.

A method for automatic and fast shot detection for the MPEG video is proposed. Shot detection is the first step in analyzing and searching a large amount of video data. Our proposed method is based on activity of images as well as intra MBs in the video frame. It is possible to detect scene changes rapidly by using information from the compressed MPEG video data without the need for full-frame decoding. Experimental results show that the proposed method can provide good performance at a low computational cost.

The current paper proposes a new method for reducing the computational overhead of fine-to-coarse multi-resolution motion estimation (MRME) on the finest resolution level based on limiting the search region used to consider the motion vectors from the coarsest resolution subband. Half-pel accuracy motion estimation (HPAME) is used in the baseband to achieve an exact motion vector, which has a significant influence on the reconstructed image. However, since this method involves a high computational overhead, the use of selective interpolation is suggested to reduce the computational overhead. The proposed method uses HPAME to estimate exact motion vectors in the baseband, then based on the characteristics of these motion vectors, the motion vectors in the higher frequency subbands are selectively estimated. That is, motion vectors in the higher frequency subbands are only estimated for those blocks with half-pixel accuracy motion vectors in the baseband. Experimental results show that the proposed algorithm can produce better image quality results than the conventional algorithms.

A novel image enhancement algorithm that can efficiently detect a small target of panoramic infrared (IR) imagery is proposed. Image enhancement is the first step for detecting and recognizing a small target in the IR imagery. The essence of the proposed algorithm is to utilize the independent histogram equalization (HE) separately over two sub-images obtained by decomposing the given image through the statistical hypothesis testing (SHT). Experimental results show that the proposed algorithm has better discrimination and lower false alarm rate than the conventional algorithms.