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Clustering-based quantisation for PDE-based image compression

  • Optimal known pixel data for inpainting in compression codecs based on partial differential equations is real-valued and thereby expensive to store. Thus, quantisation is required for efficient encoding. In this paper, we interpret the quantisation step as a clustering problem. Due to the global impact of each known pixel and correlations between spatial and tonal data, we investigate the central question, which kind of feature vectors should be used for clustering with popular strategies such as k-means. Our findings show that the number of colours can be reduced significantly without impacting the reconstruction quality. Surprisingly, these benefits are negated by an increased coding cost in compression applications.

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Metadaten
Author: Laurent HoeltgenORCiD, Pascal Peter, Michael BreußGND
DOI:https://doi.org/10.1007/s11760-017-1173-9
ISSN:1863-1703
ISSN:1863-1711
Title of the source (English):Signal, Image and Video Processing
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2018
Tag:Clustering; Compression; Inpainting; Laplace interpolation; Partial differential equations; Quantisation
Volume/Year:12
Issue number:3
First Page:411
Last Page:419
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Angewandte Mathematik
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