Texture Compression using Wavelet Decomposition

Mavridis, Pavlos; Παπαϊωάννου, Γεώργιος

doi:10.1111/j.1467-8659.2012.03203.x

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…[16,17,6] are also not able to extend textures to larger domains, since they rely on an explicit mapping between the input image/video and the summarization. Other schemes aim to compact the spectral energy into few coefficients [2,10,27]. [35] compresses regular textures, but fails with stochastic ones.…”

Section: Methods)mentioning

confidence: 99%

Texture representations for image and video synthesis

Georgiadis

Chiuso

Soatto

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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In texture synthesis and classification, algorithms require a small texture to be provided as an input, which is assumed to be representative of a larger region to be resynthesized or categorized. We focus on how to characterize such textures and automatically retrieve them. Most works generate these small input textures manually by cropping, which does not ensure maximal compression, nor that the selection is the best representative of the original. We construct a new representation that compactly summarizes a texture, while using less storage, that can be used for texture compression and synthesis. We also demonstrate how the representation can be integrated in our proposed video texture synthesis algorithm to generate novel instances of textures and video hole-filling. Finally, we propose a novel criterion that measures structural and statistical dissimilarity between textures.

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Section: Methods)mentioning

confidence: 99%

Texture representations for image and video synthesis

Georgiadis

Chiuso

Soatto

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…In the remainder of this chapter we first describe the challenges for texture compression algorithms, we discuss why traditional transform coding is unsuitable for this job, we provide an overview of existing texture compression methods and then we present our new flexible wavelet-based texture compression scheme. The work presented in this chapter has been published in [MP12d] and [MP12c].…”

Section: Texture Compression Using Wavelet Decompositionmentioning

confidence: 99%

Efficient texture representation and sampling algorithms for real-time rendering

Mavridis¹,

Μαυρίδης²

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In computer graphics, the goal of photorealistic rendering algorithms is to create convincing images given the description of a scene. However, photorealistic image generation is still an elusive goal for real-time rendering, where the final image should be synthesized in a small fraction of a second. In order to meet this performance requirement, images rendered in real-time often suffer from reduced quality, frequently manifested as low-detailed surfaces, aliasing artifacts and non-realistic illumination.The goal of this thesis is to improve the quality of real-time rendering through more efficient data representation and sampling algorithms. First, we present more compact representations for textures and frame buffers, reducing the consumed memory bandwidth and allowing more efficient use of the available memory space. Furthermore, we present and investigate a method that improves the surface detail under extreme conditions, such as grazing viewing angles, highly warped texture coordinates, or extreme perspective, using high-quality elliptical texture filtering on the GPU. Finally, we propose the use of a volume-based representation of the scene in order to simulate the diffuse light transport in real-time for completely dynamic scenes. To this end, we present efficient algorithms for the creation and sampling of this volume representation, which is stored as a volume texture on the graphics memory. JournalsPavlos Mavridis and Georgios Papaioannou, Texture compression using wavelet decomposition, Computer Graphics Forum (Proceedings of Pacific Graphics 2012) 31 (2012), no. 7 Pavlos Mavridis and Georgios Papaioannou, The compact YCoCg frame buffer,

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