Block-based compression tends to be inefficient when blocks contain arbitrary shaped discontinuities. Recently, graph-based approaches have been proposed to address this issue, but the cost of transmitting graph topology often overcome the gain of such techniques. In this work we propose a new Superpixel-driven Graph Transform (SDGT) that uses clusters of superpixels, which have the ability to adhere nicely to edges in the image, as coding blocks and computes inside these homogeneously colored regions a graph transform which is shape-adaptive. Doing so, only the borders of the regions and the transform coefficients need to be transmitted, in place of all the structure of the graph. The proposed method is finally compared to DCT and the experimental results show how it is able to outperform DCT both visually and in term of PSNR.

Superpixel-driven graph transform for image compression

VERDOJA, FRANCESCO;GRANGETTO, Marco;
2015

Abstract

Block-based compression tends to be inefficient when blocks contain arbitrary shaped discontinuities. Recently, graph-based approaches have been proposed to address this issue, but the cost of transmitting graph topology often overcome the gain of such techniques. In this work we propose a new Superpixel-driven Graph Transform (SDGT) that uses clusters of superpixels, which have the ability to adhere nicely to edges in the image, as coding blocks and computes inside these homogeneously colored regions a graph transform which is shape-adaptive. Doing so, only the borders of the regions and the transform coefficients need to be transmitted, in place of all the structure of the graph. The proposed method is finally compared to DCT and the experimental results show how it is able to outperform DCT both visually and in term of PSNR.
IEEE International Conference on Image Processing, ICIP 2015
can
2015
Proceedings - International Conference on Image Processing, ICIP
IEEE Computer Society
2015-
2631
2635
9781479983391
9781479983391
clustering; graph transform; Image compression; superpixels; Software; 1707; Signal Processing
Fracastoro, Giulia; Verdoja, Francesco; Grangetto, Marco; Magli, Enrico
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1557609
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