Synthetic images such as maps, cartoons, logos, clip art, and technical drawings are suitable for vectorization. And, just as with these other two operations, while rasterization is fairly straightforward and algorithmic, vectorization involves the reconstruction of lost information and therefore requires heuristic methods. Vectorization is the inverse operation corresponding to rasterization, as integration is to differentiation. In vectorization, the shape of the character is preserved, so artistic embellishments remain. For most applications, vectorization also does not involve optical character recognition characters are treated as lines, curves, or filled objects without attaching any significance to them. It is not examining the image and attempting to recognize or extract a three-dimensional model which may be depicted i.e. The task in vectorization is to convert a two-dimensional image into a two-dimensional vector representation of the image. Edges and filled areas are represented as mathematical curves or gradients, and they can be magnified arbitrarily (though of course the final image must also be rasterized in to be rendered, and its quality depends on the quality of the rasterization algorithm for the given inputs). Ideally, a vector image does not have the same problem. Images of sharp edges become fuzzy or jagged. The halftone dots, film grains, and pixels become apparent.
If the image is magnified enough, its artifacts appear. While such an image is useful, it has some limits. In the picture, scaling the bitmap reveals the pixels while scaling the vector image preserves the shapes.Īn image does not have any structure: it is just a collection of marks on paper, grains in film, or pixels in a bitmap.
The bitmap image is composed of a fixed set of pixels, while the vector image is composed of a fixed set of shapes. This image illustrates the difference between bitmap and vector images.
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