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Compression and interpolation each require, given part of an image, or part of a collection or stream of images, being able to predict other parts. Compression is achieved by transmitting part of the imagery along with instructions for predicting the rest of it; of course, the instructions are usually much shorter than the unsent data. Interpolation is just a matter of predicting part of the way between two extreme images; however, whereas in compression the original image is known at the encoder, and thus the residual can be calculated, compressed, and transmitted, in interpolation the actual intermediate image is not known, so it is not possible to improve the final image quality by adding back the residual image. Practical 3D-video compression methods typically use a system with four modules: (1) coding one of the streams (the main stream) using a conventional method (e.g., MPEG), (2) calculating the disparity map(s) between corresponding points in the main stream and the auxiliary stream(s), (3) coding the disparity maps, and (4) coding the residuals. It is natural and usually advantageous to integrate motion compensation with the disparity calculation and coding. The efficient coding and transmission of the residuals is usually the only practical way to handle occlusions, and the ultimate performance of beginning-to-end systems is usually dominated by the cost of this coding. In this paper we summarize the background principles, explain the innovative features of our implementation steps, and provide quantitative measures of component and system performance.

Compression and interpolation of 3D stereoscopic and multiview video