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U. Glaeser

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FIGURE 36.4<br />

The general form of the computation that this block performs is given by the following equations:<br />

weighted– sum = Sum{ wi<br />

× input_pixelI}<br />

output– pixel– value = B × input_pixel_value + A × pixel_value_from_prev_iteration − b<br />

× weighted_sum + c × weighted_sum<br />

This block takes five parameters that define its operation. The mask coefficients array holds the values<br />

of the coefficients. The parameters B,<br />

A,<br />

and c all take the value zero for all the functions except the<br />

iterative image restoration algorithm.<br />

Thresholding Block<br />

The operators in this class produce results based on a single pixel’s value. The computation is rather<br />

simple; it compares the value of the input pixel to a predetermined threshold value. The output pixel<br />

value is determined accordingly. The parameters of this block are the threshold value T and the algorithm<br />

selection input. For the image halftoning application, the threshold value T is set to be 127, where the<br />

pixel values range between 0 (black) and 255 (white). If the pixel value is above threshold, output is given<br />

as 255, otherwise it is 0. For the edge detection operation, the output is set equal to the input value if<br />

the pixel value is above threshold, and to 0 otherwise.<br />

Pixel Modification Block<br />

Pixel modification operations are point-processing operations. This block performs darkening, lightening,<br />

and negation of images. It takes two parameters, J and algorithm selection input ALG. For the<br />

darkening operation a positive value J is added to the input pixel value. Lightening is achieved by subtracting<br />

J from each input pixel. The negative of an image is achieved by subtracting the input pixel value from<br />

J,<br />

which takes the value 255 for this case.<br />

36.6 Experiments<br />

In this section, the author presents the experiments to estimate the potential gain in reconfiguration time<br />

that our SPS architecture will yield. This is an exploration of the reduction in reconfiguration bits that<br />

would follow as a result of providing preplaced computation blocks with our coarse grain architecture.<br />

Application Profiling<br />

First, a profiling has been done on the image processing benchmarks in order to gain insight into what<br />

type of components are used more frequently. Such a profile can give directives to the architecture formation<br />

tool and guide it to employ certain blocks. Looking at the numbers and types of components that were<br />

selected by our scheduling tool, we have obtained the component usage profile as shown in Fig. 36.5.<br />

© 2002 by CRC Press LLC

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