Arithmetic Edge Coding for Arbritrarily Shaped Sub-block Motion Prediction in Depth Video Compression

Ismael Daribo, Gene Cheung, and Dinei Florencio

Abstract

Depth map compression is important for compact representation of

3D visual data in “texture-plus-depth” format, where texture and

depth maps of multiple closely spaced viewpoints are encoded and

transmitted. A decoder can then freely synthesize any chosen intermediate

view via depth-image-based rendering (DIBR) using neighboring

coded texture and depth maps as anchors. In this work, we

leverage on the observation that “pixels of similar depth have similar

motion” to efficiently encode depth video. Specifically, we divide a

depth block containing two zones of distinct values (e.g., foreground

and background) into two sub-blocks along the dividing edge before

performing separate motion prediction. While doing such arbitrarily

shaped sub-block motion prediction can lead to very small prediction

residuals (resulting in few bits required to code them), it incurs

an overhead to losslessly encode dividing edges for sub-block identification.

To minimize this overhead, we first devise an edge prediction

scheme based on linear regression to predict the next edge

direction in a contiguous contour. From the predicted edge direction,

we assign probabilities to each possible edge direction using the von

Mises distribution, which are subsequently inputted to a conditional

arithmetic codec for entropy coding. Experimental results show an

average overall bitrate reduction of up to 30% over classical H.264

implementation.

Details

Publication typeInproceedings
Published inICIP
PublisherIEEE International Conf. on Image Processsing - ICIP
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