Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree
This work addresses noise and bandwidth issues in transmitting image contours for computer vision applications, but it is incremental as it builds on existing contour processing techniques.
The paper tackled the problem of noisy object contours from depth images by proposing a joint denoising and compression method, which theoretically and experimentally outperformed separate approaches in rate-distortion performance.
With the advent of depth sensing technologies, the extraction of object contours in images---a common and important pre-processing step for later higher-level computer vision tasks like object detection and human action recognition---has become easier. However, acquisition noise in captured depth images means that detected contours suffer from unavoidable errors. In this paper, we propose to jointly denoise and compress detected contours in an image for bandwidth-constrained transmission to a client, who can then carry out aforementioned application-specific tasks using the decoded contours as input. We first prove theoretically that in general a joint denoising / compression approach can outperform a separate two-stage approach that first denoises then encodes contours lossily. Adopting a joint approach, 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'). We design a dynamic programming (DP) algorithm that solves the posed problem optimally, and propose a compact context representation called total suffix tree (TST) that can reduce complexity of the algorithm dramatically. Experimental results show that our joint denoising / compression scheme outperformed a competing separate scheme in rate-distortion performance noticeably.