
TL;DR
This paper introduces quantisation scale-spaces based on hierarchical grey-value sparsification, which are linked to information theory and improve image compression over traditional methods.
Contribution
It presents a novel sparsification algorithm for grey-value domains that outperforms uniform quantisation and classical clustering.
Findings
Quantisation scale-spaces are effective for image inpainting-based compression.
The proposed algorithm surpasses traditional uniform quantisation.
Hierarchical quantisation improves coding efficiency.
Abstract
Recently, sparsification scale-spaces have been obtained as a sequence of inpainted images by gradually removing known image data. Thus, these scale-spaces rely on spatial sparsity. In the present paper, we show that sparsification of the co-domain, the set of admissible grey values, also constitutes scale-spaces with induced hierarchical quantisation techniques. These quantisation scale-spaces are closely tied to information theoretical measures for coding cost, and therefore particularly interesting for inpainting-based compression. Based on this observation, we propose a sparsification algorithm for the grey-value domain that outperforms uniform quantisation as well as classical clustering approaches.
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