Friday, February 20, 1998
3:00 - 4:00 pm
Desktop imaging systems have become ubiquitous. There are many interesting signal processing problems associated with the task of rendering high quality images with these systems. After a brief overview of this area, I will focus on two such problems in particular. The first is the efficient implementation of the highly nonlinear transformations between a standard color space such as CIE L*a*b* and the native space of a color printer. This is a problem in multidimensional approximation theory. Our method which we call sequential linear interpolation is mediated by the need for extremely fast search and computation, as well as the desire to minimize the number of grid points in the lookup tables. The approach is inspired by the asymptotic theory of vector quantization. We previously developed design rules that are optimal under a mean-squared error criterion. I will describe our more recent results that are optimal in a minimax sense. The second problem that I will discuss is that of digital halftoning, which determines the actual bit map sent to the printer. Our approach is based on a heuristic search for the bit map that achieves a local minimum of a cost function based on a simple model for the human visual system. This method yields halftone images of excellent quality. Despite the apparent complexity of the algorithm, I will show that it can be implemented with urprising efficiency. In addition, I will briefly describe derivative algorithms that are still more efficient, requiring only a few operations per pixel.
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