  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.
