Distributed stereo image coding with improved disparity and noise estimation

Distributed coding of correlated grayscale stereo images is effectively addressed by a recently proposed codec that learns block-wise disparity at the decoder. Based on the Slepian-Wolf theorem, one image can be transmitted at a rate approaching the conditional entropy if the other image is referenced as side information at the decoder. This paper improves the methods in the decoder design by refining disparity estimates to pixel resolution, generating more accurate initial disparity estimates, and modeling noise as a nonstationary random field. The new decoder enables up to an additional 9 percent bit rate savings for lossless coding. When the rate is insufficient for lossless reconstruction, the new decoder improves PSNR and significantly reduces visually unpleasant blocking artifacts.