Kinect-Like Depth Compression with 2D+T Prediction

The Kinect-like depth compression becomes increasingly important due to the growing requirement on Kinect depth data transmission and storage. Considering the temporal inconsistency of Kinect depth introduced by the random depth measurement error, we propose 2D+T prediction algorithm aiming at fully exploiting the temporal depth correlation to enhance the Kinect depth compression efficiency. In our 2D+T prediction, each depth block is treated as a subsurface, and it the motion trend is detected by comparing with the reliable 3D reconstruction surface, which is integrated by accumulated depth information stored in depth volume. The comparison is implemented under the error tolerant rule, which is derived from the depth error model. The experimental results demonstrate our algorithm can remarkably reduce the bitrate cost and the compression complexity. And the visual quality of the 3D reconstruction results generated from our reconstructed depth is similar to that of traditional video compression algorithm.