Joint denoising / compression of image contours via geometric prior and variable-length context tree

Abstract

The advent of depth sensing technologies has eased the detection of object contours in images. For efficient image compression, coded contours can enable edge-adaptive coding techniques such as graph Fourier transform (GFT) and arbitrarily shaped sub-block motion prediction. However, acquisition noise in captured depth images means that detected contours also suffer from errors. In this paper, we propose to jointly denoise and compress detected contours in an image. Specifically, we first propose a burst error model that models typical errors encountered in an observed string y of directional edges. We then formulate a rate-constrained maximum a posteriori (MAP) problem that trades off the posterior probability P(x|y) of an estimated string x given y with its code rate R(x). Given our burst error model, we show that the negative log of the likelihood P(y|x) can be written as a simple sum of burst error events, error symbols and burst lengths, while the geometric prior P(x) states intuitively that contours are more likely straight than curvy. We design a dynamic programming (DP) algorithm that solves the posed problem optimally. Experimental results show that our joint denoising / compression scheme outperformed a competing separate scheme in rate-distortion performance noticeably.

DOI: 10.1109/ICIP.2016.7532618

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Cite this paper

@article{Zheng2016JointD, title={Joint denoising / compression of image contours via geometric prior and variable-length context tree}, author={Amin Zheng and Gene Cheung and Dinei A. F. Flor{\^e}ncio}, journal={2016 IEEE International Conference on Image Processing (ICIP)}, year={2016}, pages={1549-1553} }