We integrate stylized rendering with an efficient multiresolution
image representation, enabling a user to control how
compression affects the aesthetic appearance of an image. We
adopt a point-based rendering approach to progressive image transmission
and compression. We use a novel, adaptive farthest point
sampling algorithm to represent the image at progressive levels of
detail, balancing global coverage with local precision. A progressively
generated discrete Voronoi diagram forms the common foundation
for our sampling and rendering framework. This framework
allows us to extend traditional photorealistic methods of image reconstruction
by scattered data interpolation to encompass nonphotorealistic
rendering. It supports a wide variety of artistic rendering
styles based on geometric subdivision or parametric procedural textures.
Genetic programming enables the user to create original rendering
styles through interactive evolution by aesthetic selection.
We compare our results with conventional compression, and we
discuss the implications of using nonphotorealistic representations
for highly compressed imagery.
By integrating stylized rendering with an efficient multiresolution image representation, we enable the user to control how compression affects the aesthetic appearance of an image. Adopting a point-based rendering approach to progressive image transmission and compression, we represent an image by a sequence of color values. To best approximate the image at progressive levels of detail, a novel, adaptive farthest point sampling algorithm balances global coverage with local precision. Without storing any spatial information apart from the aspect ratio, the spatial position of each color value is inferred from the preceding members of the sampling sequence. Keeping track of the spatial influence of each sample on the rendition, a progressively generated discrete Voronoi diagram forms the common foundation for our sampling and rendering framework. This framework allows us to extend traditional photorealistic methods of image reconstruction by scattered data interpolation to encompass non-photorealistic rendering. It supports a wide variety of artistic rendering styles based on geometric subdivision or parametric procedural textures. Genetic programming enables the user to create original rendering styles through interactive evolution by aesthetic selection. Comparing our results with JPEG, we conclude with a brief overview of the implications of using non-photorealistic representations for highly compressed imagery.
Histogram warping is a novel histogram specification technique for use in color image processing. As a general purpose tool for color correction, our technique constructs a global color mapping function in order to transform the colors of a source image to match a target color distribution to any desired degree of accuracy. To reduce the risk of color distortion, the transformation takes place in an image dependent color space, featuring perceptually uniform color axes with statistically independent chromatic components. Eliminating the coherence between the color axes enables the transformation to operate independently on each color axis. Deforming the source color distribution to reproduce the dominant color features of the target distribution, the histogram warping process is controlled by designating the color shifts and contrast adjustments for a set of key colors. Assisted by mode detection, matching quantiles establish the correspondence between the color distributions. Interpolation by monotonic splines serves to extend the mapping over the entire dynamic range without introducing artificial discontinuities into the resulting color density. We show how our method can be applied to color histogram equalization as well as color transfer from an example image or a color palette.
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