Neighboring Pixel Values Research Articles

A fuzzy logic-based predictor for predictive coding of images

In this paper, we present a fuzzy logic-based nonlinear predictor for predictive coding of images. We define five local structure patterns of images: uniform area, horizontal contour (0/spl deg/), vertical contour (90/spl deg/), 45/spl deg/, and 135/spl deg/ diagonal contours. Their membership functions are derived with the gradient-based edge detection method and predicted values for different patterns are defined by linear extrapolation from available neighborhood pixel values. The predicted value of the current pixel can be obtained based on the membership functions and the defined predicted values for the different patterns. A set of parameters to characterize the proposed fuzzy predictor are determined from empirical data. Success in the use of the proposed predictor is demonstrated, by using simulation results through the reduction in the entropy as compared to those of existing linear and nonlinear ones. It is also shown that the proposed fuzzy predictor can be efficiently implemented.

Two‐dimensional neural hybrid filters using adaptive windows and layered median filters

AbstractA new 2‐D neural hybrid filter tha combines a layered median filter with adaptive‐window linear filters is described. the filter can eliminate both impulsive and Gaussian noise and has good edge preservation characteristics, owing in particular to the 3‐point layered median filter design. the size of the adaptive windows is maximized in regions where the neighboring pixel values differ relatively little from that at the working point. the design yields effective noise removal without edge degradation, and learning by the neural network allows the filter to adapt to image characteristics.

Differentiation applied to lossless compression of medical images

Lossless compression of medical images using a proposed differentiation technique is explored. This scheme is based on computing weighted differences between neighboring pixel values. The performance of the proposed approach, for the lossless compression of magnetic resonance (MR) images and ultrasonic images, is evaluated and compared with the lossless linear predictor and the lossless Joint Photographic Experts Group (JPEG) standard. The residue sequence of these techniques is coded using arithmetic coding. The proposed scheme yields compression measures, in terms of bits per pixel, that are comparable with or lower than those obtained using the linear predictor and the lossless JPEG standard, respectively, with 8-b medical images. The advantages of the differentiation technique presented here over the linear predictor are: 1) the coefficients of the differentiator are known by the encoder and the decoder, which eliminates the need to compute or encode these coefficients, and 21 the computational complexity is greatly reduced. These advantages are particularly attractive in real time processing for compressing and decompressing medical images.

Context-based Lossless Image Compression

The two-dimensional method of Langdon and Rissanen for compression of black and white images is extended to handle the exact lossless compression of grey-scale images. Neighbouring pixel values are used to define contexts and probabilities associated with the contexts are used to compress the image. The problem of restricting the number of contexts, both to limit the storage requirements and to be able to obtain sufficient data to generate meaningful probabilities, is addressed. Investigations on a variety of images are carried using the JPEG lossless mode predictors. Results indicate that a one pass arithmetic encoder using restricted contexts can lead to effective lossless image compression algorithms.

Digital Step Edges from Zero Crossing of Second Directional Derivatives

We use the facet model to accomplish step edge detection. The essence of the facet model is that any analysis made on the basis of the pixel values in some neighborhood has its final authoritative interpretation relative to the underlying gray tone intensity surface of which the neighborhood pixel values are observed noisy samples. With regard to edge detection, we define an edge to occur in a pixel if and only if there is some point in the pixel's area having a negatively sloped zero crossing of the second directional derivative taken in the direction of a nonzero gradient at the pixel's center. Thus, to determine whether or not a pixel should be marked as a step edge pixel, its underlying gray tone intensity surface must be estimated on the basis of the pixels in its neighborhood. For this, we use a functional form consisting of a linear combination of the tensor products of discrete orthogonal polynomials of up to degree three. The appropriate directional derivatives are easily computed from this kind of a function. Upon comparing the performance of this zero crossing of second directional derivative operator with the Prewitt gradient operator and the Marr-Hildreth zero crossing of the Laplacian operator, we find that it is the best performer; next is the Prewitt gradient operator. The Marr-Hildreth zero crossing of the Laplacian operator performs the worst.