In distributed video source coding side-information at the decoder is generated as a temporal prediction based on previous frames. This creates a virtual dependency channel between the source video at the encoder and the side information at the decoder. In recent years, distributed source coders were introduced with sophisticated error correction codes, like Turbo Codes and LDPC codes. Although these codes performs well on noisy network communication channels, it is far from obvious that these codes can handle the non-stationary noise in the dependency channel as encountered in distributed video coders. In this paper we study the consequences of inaccurate modeling of the dependency channel on Turbo and LDPC coding and show that the performance depends greatly on the choice of the probabilistic model for the dependency channel. The results show that LDPC codes are less sensitive to inaccuracies in the dependency channel models.
In distributed video coding, the complexity of the video encoder is reduced at the cost of a more complex video decoder. Using the principles of Slepian and Wolf, video compression is then carried out using channel coding principles, under the assumption that the video decoder can temporally predict side-information that is correlated with the source video frames. In recent work on distributed video coding the application of turbo codes has been studied. Turbo codes perform well in typical (tele-)communications settings. However, in distributed video coding the dependency channel between source and side-information is inherently non-stationary, for instance due to occluded regions in the video frames. In this paper, we study the modeling of the virtual dependency channel, as well as the consequences of incorrect model assumptions on the turbo decoding process. We observe a strong dependency of the performance of the distributed video decoder on the model of the dependency channel.
Research in distributed video coding for low complexity encoding has shown that without knowledge of the correlation between source and side information (i.e. the behavior of the dependency channel), the performance is substantially below that of well known state-of-the-art video coders. In a practical system the decoder needs to estimate the statistics in this dependency channel. In this paper we investigate the relation between the compression ratio and the sensitivity of the estimated channel model parameter at the decoder side. We observe that this is a hard task, but not unrealistic. We show that the tolerable parameter range is very dependent on the compression ratio and the (actual) statistics in the dependency channel.
The ongoing research in Distributed Video Coding (DVC) for low complexity encoding is trying to shorten the substantial performance gap to well known state-of-the-art coders. One way of reducing this gap is to improve the quality of the motion compensated prediction. In this paper we investigate which motion estimation method to apply in DVC, comparing possible methods to produce a motion compensated prediction. We use interpolation as well as extrapolation methods. Our results show that even with a very simple DCT scheme for the Wyner Ziv frames, extrapolation can outperform the widely used interpolation.
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