Rojtberg, Pavel (2012)
Image Compression Using MRF Priors.
Technische Universität Darmstadt
Masterarbeit, Bibliographie
Kurzbeschreibung (Abstract)
Markov Random Field (MRF) models are used in many low-level computer-vision problems like inpainting or denoising. In this work we evaluate the use of MRF natural image priors in the context of image compression. To this end we formulate compression as finding a sparse point representation of the image, while decompression is formulated as MRF based inpainting. For finding a sparse point representation of images we consider using entropy of the prior probability and the variance of the probabilistic expert functions. The results here are competitive with existing methods. For decompression we find the ability to generate structures of the used high order MRF based model to be lacking. However our experiments with a mean modulating model indicate that, generating more structures is possible for inpainting. Furthermore we adapt the binary tree triangular coding for a variance based point selection and use it to evaluate the importance of efficiently storing the sparse point representation of the image. Here we show that using WebP lossless compression is just as adequate to store the sparse point representation in practical cases. An evaluation considering the state of the art lossy JPEG2000 codec however reveals that our MRF prior based method has to be improved further to be competitive in qualitative terms.
| Typ des Eintrags: | Masterarbeit |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Rojtberg, Pavel |
| Art des Eintrags: | Bibliographie |
| Titel: | Image Compression Using MRF Priors |
| Sprache: | Englisch |
| Publikationsjahr: | 2012 |
| Kurzbeschreibung (Abstract): | Markov Random Field (MRF) models are used in many low-level computer-vision problems like inpainting or denoising. In this work we evaluate the use of MRF natural image priors in the context of image compression. To this end we formulate compression as finding a sparse point representation of the image, while decompression is formulated as MRF based inpainting. For finding a sparse point representation of images we consider using entropy of the prior probability and the variance of the probabilistic expert functions. The results here are competitive with existing methods. For decompression we find the ability to generate structures of the used high order MRF based model to be lacking. However our experiments with a mean modulating model indicate that, generating more structures is possible for inpainting. Furthermore we adapt the binary tree triangular coding for a variance based point selection and use it to evaluate the importance of efficiently storing the sparse point representation of the image. Here we show that using WebP lossless compression is just as adequate to store the sparse point representation in practical cases. An evaluation considering the state of the art lossy JPEG2000 codec however reveals that our MRF prior based method has to be improved further to be competitive in qualitative terms. |
| Freie Schlagworte: | Probabilistic models, Markov random fields (MRF), Image compression, Image interpolation, Image restoration, Image synthesis |
| Zusätzliche Informationen: | 59 p. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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