In this paper, a video scrambling method is proposed. It is realized by separating a video fingerprint when a video is encoded into MPEG-2 stream. A concept of “Video Fingerprint” as the key information to distinguish a video has already been proposed. In the proposed method, the head DC factors of every slice of intra coded frames are manipulated using the value made from a video fingerprint. The MPEG streams those are encoded with the proposed method are based on the MPEG syntax, but they cannot be decoded normally without a video fingerprint. Proposed method reduces PSNR 10.6 dB from 31.9 dB to 20.3 dB applied to nine test sequences that are used MPEG-2 standardization. In proposed method, the amount of information to indicate the method to manipulate DC prediction value is increaded, but the amount of VLC of DC prediction value is decreased, the relationship is also shown in this paper.

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In this paper, we propose a new parameter estimation method for space-variant motion degraded image. At the first step, the sub-block image is defined for motion parameter estimation in an original image. Over this sub-block image, its 2-D power spectrum is calculated. Then, the degraded direction of motion is estimated to be a direction where the power ratio becomes the maximum. Here the power ratio is the ratio of a power spectrum of an estimated degradation direction to a power spectrum which is orthogonal. Secondly, to obtain the degraded motion length, its cepstrum is analyzed. The degraded length is estimated as the position where the maximum value of the cepstrum in the direction of degradation previously determined is achieved. In addition, we propose to use the scale space image using LOG operator to an observation image for obtaining this result more stably. As the results of some simulation experiments, the effectiveness of our proposal method can be concretely confirmed.

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A data-driven multi-dimensional histogram method is proposed for segmentation of images observed in a multi-dimensional space. An image is observed as a mixture of multi-variate data, but the number of clusters in the mixture is generally unknown. In addition, the histogram width needs to be determined by a criterion on the basis of the observed image data information. The proposed algorithm shows criteria to determine the histogram width and the number of clusters according to the observed multi-variate data. Simultaneously, the statistics for each cluster are computed, which are used for clustering all of the other elements. The proposed clustering method can be applied to various kinds of images, because it is not restricted by an image model used in model-based approaches. The effectiveness of the algorithm is evaluated by computer simulation, and the algorithm is aplied to real color image clustering.

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Stereoscopic displays showing the stereoscopic images for plural one without glasses are required in various fields. We have developed a new type of such stereoscopic display. It consists of a pair of parallax image panels, a pair of reflecting-converging devises and a front light source. The parallax image panels and the light source form a pair of full resolution parallax images to display the stereoscopic image in the eyes of the observers with the reflecting-converging devices. So this stereoscopic display have the characteristics of high resolution and compactness comparatively.

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