Stylized rendering for multiresolution image representation
| Autor: | Chris Gibbs, Neil A. Dodgson, Mark Grundland |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
business.industry
Artistic rendering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image-based modeling and rendering Real-time rendering 3D rendering Rendering (computer graphics) Image-based lighting Computer graphics (images) Computer vision Tiled rendering Artificial intelligence business Image restoration ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
| Zdroj: | Human Vision and Electronic Imaging |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.596817 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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