Compression of Noisy Depth Image using Planes
Kanika Modi, Prem Kumar Kalra, Subodh Kumar · 2014
This paper investigates compression of depth images – particularly, noisy depth images – captured by depth sensors like Kinect. Our scheme is based on incrementally detecting planes in depth images and then storing the plane parameters instead of the depth information of the pixels lying on the detected planes. Residuals are then computed and compressed using standard image compression techniques. Our technique incorporates the input error model for comprehensive and accurate plane detection. Thereby, this accounts for the reliability of the input data in the compression scheme. The plane detection also accounts for edges. Experiments exhibit better image quality than standard compression techniques with smaller error. We additionally propose a novel error metric to evaluate compression of noisy depth images.