DVC using a half-feedback based approach

Distributed video coding has become increasingly popular in recent years among the researchers in video coding due to its attractive and promising features. DVC proposed a dramatic structural change to video coding by shifting the majority of complexity conventionally residing in the encoder towards the decoder. Nevertheless, these kinds of architectures have some serious limitations that hinder its practical application. The uses of a feedback channel between the encoder and the decoder requires an interactive decoding procedure which is a limitation for certain applications such as offline processing. On the other hand, the decoder needs an efficient way to estimate the probability of error without assuming the availability of the original video at the decoder. In this paper we investigate a first approximation to solve both problems based on the use of machine learning to extract the knowledge that exits between the residual frame and the number of requests over this feedback channel. Exploiting this correlation gives us a more practical architecture without higher complexity encoders. We apply these concepts to pixel-domain Wyner-Ziv coding and the results show a loss of 0.21 dB in the rate-distortion performance.