Distributed Compression of Stereoscopic Images with Unsupervised Learning of Disparity

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. In this paper, we propose an Expectation Maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Experimental results show that this performs nearly as well as a system which knows the disparity through an oracle.