Decoding Approach With Unsupervised Learning Of Two Motion Fields For Improved Wyner-Ziv Coding Of Video

Wyner-Ziv video coding (WZVC) is a video coding paradigm allows exploiting the source statistic, partially or totally, at the decoder to reduce the computational burden at the encoder. Side information (SI) generation is a key function in the WZVC decoder, and plays an important role in determining the performance of the codec. In this context, this paper proposes decoding approach with unsupervised learning of two motion fields to improve the accuracy of generation of soft SI on WZVC codec. The method used in this paper is based on the generalization of Expectation-Maximization (EM) algorithm, in which the learning process of motion fields used the Low Density Parity-Check (LDPC) decoder soft output values and two frames previously decoded as initial SI. In this method, the decoder always updated the accuracy of soft SI by renewing two motion fields iteratively. The goal is to minimize the transmission of the bits required by the decoder to estimate frame WZ. The experimental results show that the proposed codec WZVC could improve performance rate-distortion (RD) and lower the bit transmission compared to the existing WZVC. Keywords: Wyner-Ziv Video Coding, Unsupervised learning, EM Algorithm, Side Information, LDPC