Distributed Coding of Random Dot Stereograms with Unsupervised Learning of Disparity
David Varodayan, Aditya Mavlankar, Markus Flierl, Bernd Girod · 2006
Distributed compression is particularly attractive for stereoscopic images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. We consider a toy problem, the compression of random dot stereograms, and propose an expectation maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Our experiments show that this can achieve twice as efficient compression compared to a system with no disparity compensation and perform nearly as well as a system which knows the disparity through an oracle