In the ultrasound diagnosis field, an analysis of speckle image texture in ultrasound B-scans is one of the important researches using stochastic signal processing method. In this paper, it is focused on finding out theoretically the probability density function of the speckle image texture for the magnitude. First, the occurrence mechanism of speckle image texture is treated as a random walk problem in an multi-dimensional signal space based on Shannon's sampling theorem, by newly introducing a Hankel transformation type characteristic function. As a result , a unified probability density function expression has been explicitly proposed and it has been clarified that the present expression agrees with the well-known Rayleigh and Rician probability density functions as two special cases. Then, the legitimacy and the effectiveness of proposed stochastic analysis method has been experimentally confirmed by applying it to the speckle pattern of clinical B-scan image actually utilized for diagnostic purpose.

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In this paper, a digital video post production system "DPPS" is proposed. DPPS is aiming at providing tools for making visual presentation efficiently. The major feature of the DPPS is, high-quality video editing, high speed Computer Graphics, easy visual effecting and post recording. The system has been realized on FM-TOWNS attached by parallel processors for visual processing and Paralleled hard-disk Systems for temporally digital storage.

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This paper presents a new region segmentation coding based on HVC color space. To segment an image into several characteristic regions, we adopt the segmentation by K-mean clustering algorithm in HVC color space and region merging process based on the measure of Godlove's color difference. The region segmentation coding scheme are as follows; 1) We encode the contour information of the region by using the chain-code. 2) Hue signals within one region are replaced by one representative Hue signal. 3) To encode V signals, we adopt the adaptive DCT coding scheme. 4) The Chroma signal within one region is approximated by the two-dimensional polynomial function. We encode the polynomial coefficients for each region. A satisfactory quality images are obtained by the following coding rate : 0.6 - 2.0[bit/pixel].

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This paper presents a Component-Interlaced Vector Quantization (CIVQ) scheme for color image data compression. The CIVQ utilizes the inter-component correlations and local color features of image signals to reduce the computational complexity and to improve the coding performance. The three k-dimensional component vectors of a color image are subsampled and spatially interlaced into a single k-dimensional vector. And then, vector quantization is performed on this newly formed vector set. The receiver directly obtains the three quantized component vectors from the 3k-dimensional codebook which is designed by using a nonlinear interpolative technique. Two CIVQ experiments are conducted by using the mean-separated VQ and predictive VQ, respectively. Meanwhile, two color coordinate systems, RGB and YUV, are considered as well. The computer simulation results show that, in the case of k=16, a good quality of the reconstructed image can be achieved at 0.5 bit per color pixel.

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