solidot新版网站常见问题,请点击这里查看。
消息
本文已被查看3838次
Graph Transform Learning for Image Compression. (arXiv:1712.06393v1 [cs.IT])
来源于:arXiv
In this paper, we propose a new graph-based coding framework and illustrate
its application to image compression. Our approach relies on the careful design
of a graph that optimizes the overall rate-distortion performance through an
effective graph-based transform. We introduce a novel graph estimation
algorithm, which uncovers the connectivities between the graph signal values by
taking into consideration the coding of both the signal and the graph topology
in rate-distortion terms. In particular, we introduce a novel coding solution
for the graph by treating the edge weights as another graph signal that lies on
the dual graph. Then, the cost of the graph description is introduced in the
optimization problem by minimizing the sparsity of the coefficients of its
graph Fourier transform (GFT) on the dual graph. In this way, we obtain a
convex optimization problem whose solution defines an efficient transform
coding strategy. The proposed technique is a general framework that can be
appl 查看全文>>