Compression of Depth Maps with Segment-Based Homogeneous Diffusion

Abstract

Abstract. The efficient compression of depth maps is becoming more and more important. We present a novel codec specifically suited for this task. In the encoding step we segment the image and extract betweenpixel contours. Subsequently we optimise the grey values at carefully selected mask points, including both hexagonal grid locations as well as freely chosen points. We use a chain code to store the contours. For the decoding we apply a segment-based homogeneous diffusion inpainting. The segmentation allows parallel processing of the individual segments. Experiments show that our compression algorithm outperforms comparable methods such as JPEG or JPEG2000, while being competitive with HEVC (High Efficiency Video Coding).

Compressing Flow Fields with Edge-Aware Homogeneous Diffusion Inpainting

“…A similar approach by Mainberger et al [6] for cartoon-like images also selects mask pixels along edges. Hoffmann et al [7] extended this idea by explicitly storing segment boundaries with chain codes and selecting mask pixels on a hexagonal grid.…”

“…The result of this edge-aware homogeneous diffusion inpainting gives us a flow field with discontinuities along edges E and smooth transitions in between. In contrast to the segment-based homogeneous diffusion of Hoffmann et al [7], our method allows arbitrary edge structures and does not require a segmentation with closed contours.…”

“…1). An alternative to storing edges as binary images are chain codes as used by Hoffmann et al [7]. These are very efficient in our setting as there are only three possible directions to follow.…”

“…They only store a few selected pixels and reconstruct missing data by interpolation, also called inpainting. Especially specialized approaches that use additional edge information excel for piecewise smooth images [6], [7].…”

Beyond pure quality: Progressive modes, region of interest coding, and real time video decoding for PDE-based image compression

“…[3,4,5,6,7]), most of the early works do not consider applications in image compression. Only recently it has been shown that edge-or segment-based homogeneous diffusion approaches can beat JPEG 2000 for cartoon-like im-1 ages [8], as well as for depth maps [9,10,11]. Apart from these linear, featurebased approaches, there are some attempts to use nonlinear PDEs for image compression.…”