In this paper, we proposed an efficient coding method for digital hologram (fringe pattern) acquired with a CCD camera or by computer generation using multi-view prediction and MPEG video compression standard techniques. It processes each R, G, or B color component separately. The basic processing unit is a partial image segmented as the size of MN. Each partial image retains the information of the whole object. This method generates an assembled image for a column of the segmented and frequency-transformed partial images, which is the basis of the coding process. That is, a motion estimation and compensation technique of MPEG is applied between the reconstructed images from the assembled images with the disparities found during generation of assembled image and the original partial images. Therefore the compressed results are the disparity of each partial image to form the assembled image for the corresponding column, assembled image, and the motion vectors and the compensated image for each partial image. The experimental results with the implemented algorithm showed that the proposed method has NC (Normalized Correlation) values about 4% higher than the previous method at the same compression ratios, which convinced us that ours has better compression efficiency. Consequently, the proposed method is expected to be used effectively in the application areas to transmit or store in digital format the digital hologram data.

In this paper, we propose a wavelet-based fast motion estimation algorithm for video sequence encoding with a low bit-rate. By using one of the properties of wavelet transform, multi-resolution analysis (MRA), and the spatial interpolation of an image, we can simultaneously reduce the prediction error and the computational complexity inherent in video sequence encoding. In addition, by defining a significant block (SB) based on the differential information of wavelet coefficients between successive frames, the proposed algorithm enables us to make up for the increase in the number of motion vectors when the MRME algorithm is used. As a result, we are not only able to improve the peak signal-to-noise ratio (PSNR), but also reduce the computational complexity by up to 67%.