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This paper reviews recent advances in using B pictures in the context of the draft H.26L video compression standard. We focus on reference picture selection and linearly combined motion-compensated prediction signals. We show that bi-directional prediction exploits partially the efficiency of combined prediction signals whereas multihypothesis prediction allows a more general form of B pictures. The general concept of linearly combined prediction signals chosen from an arbitrary set of reference pictures can further improve the H.26L test model TML-9 which is used in the following. We outline H.26L macroblock prediction modes for B pictures, classify them into four groups and compare their efficiency in terms of rate-distortion performance. When investigating multihypothesis prediction, we show that bidirectional prediction is a special case of this concept. Multihypothesis prediction allows also two combined forward prediction signals. Experimental results show that this case is also advantageous in terms of compression efficiency. The draft H.26L video compression standard offers improved entropy coding by context-based adaptive binary arithmetic coding. Simulations show that the gains by multihypothesis prediction and arithmetic coding are additive. B pictures establish an enhancement layer and are predicted from reference pictures that are provided by the base layer. The quality of the base layer influences the ratedistortion trade-off for B pictures. We demonstrate how the quality of the B pictures should be reduced to improve the overall rate-distortion performance of the scalable representation.

Distributed compression is particularly attractive for stereo 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 the compression of grayscale stereo images, and develop an Expectation Maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Towards this, we devise a novel method for joint bitplane distributed source coding of grayscale images. Our experiments with both natural and synthetic 8-bit images show that the unsupervised disparity learning algorithm outperforms a system which does no disparity compensation by between 1 and more than 3 bits/pixel and performs nearly as well as a system which knows the disparity through an oracle.

This article explores the efficiency of motion-compensated three-dimensional transform coding, a compression scheme that employs a motion-compensated transform for a group of pictures. We investigate this coding scheme experimentally and theoretically. The practical coding scheme employs in temporal direction a wavelet decomposition with motion-compensated lifting steps. Further, we compare the experimental results to that of a predictive video codec with single-hypothesis motion compensation and comparable computational complexity. The experiments show that the 5/3 wavelet kernel outperforms both the Haar kernel and, in many cases, the reference scheme utilizing singlehypothesis motion-compensated predictive coding. The theoretical investigation models this motion-compensated subband coding scheme for a group of K pictures with a signal model for K motion-compensated pictures that are decorrelated by a linear transform. We utilize the Karhunen-Loeve transform to obtain theoretical performance bounds at high bit-rates and compare to both optimum intra-frame coding of individual motion-compensated pictures and single-hypothesis motion-compensated predictive coding. The investigation shows that motion-compensated threedimensional transform coding can outperform predictive coding with single-hypothesis motion compensation by up to 0:5 bit=sample: r

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