A Novel Geometry Image Coding

Shi, Yong; Wen, Wen; Yin, Baocai; Shi, Jijun

doi:10.1007/978-3-642-04875-3_31

Cited by 2 publications

(2 citation statements)

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“…In our context, the signal is the geometry itself, therefore signal approximation error is the approximation error defined in the section of Introduction. Shi et al [8] used an iterative algorithm in the parameterization phase to improve the quality of geometry images and introduced a geometry image coding which utilized the image compression standard JPEG2000. In this paper, we also present a parameterization method which promotes the smoothness of texture in geometry images.…”

Section: Related Workmentioning

confidence: 99%

“…Approximation error is an essential measurement to evaluate the quality of a geometry image, which is the difference between an original mesh and its reconstruction from the sampling texture. In order to decrease the approximation error, multi-charts geometry images [3][4] [5], the signal specialized parameterization [6] [7] and geometry image coding [8] are proposed. Actually, the smoothness of texture is also an important measurement of geometry images.…”

Section: Introductionmentioning

confidence: 99%

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An Adapted Parameterization for Smooth Geometry Images

Sun

Wang

Cao

et al. 2013

2013 International Conference on Computer-Aided Design and Computer Graphics

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Geometry images are important representations of 3D geometry models. Smooth geometry images contributes to low approximation error and high image compressibility. We present an adapted parameterization method to generate a smooth geometry image. It is quite challenging to directly modify the parameter domain to smooth geometry images. Our novel idea is that we use an indirect way to construct a resulting parameter domain according to the desired geometry image. We first move image pixels according to the current parameter domain to decrease the local linear error. Then we formulate a relationship between the moved image pixels and the current parameter domain. Finally, we use the relationship to update the parameter domain by restituting the image pixels to their original positions. The process will continue until the local linear error is less than a given threshold or the number of iterations is larger than a given threshold. Experimental results illustrate that geometry images generated by our method have low linear errors and low approximation errors under different sampling resolutions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%