Addressing the uncertainty in critical rate estimation for pixeldomain WynerZiv video coding Distributed video coding is typically treated as a channel coding problem among others. The encoder generates parity bits (or syndrome bits) for the source and transmits part of them to the decoder for a certain target quality. The decoder tries to reconstruct the source using the received parity bits along with the side information available. In this paper, we aim to estimate the critical number of parity bits to transmit. Having observed the uncertainty of the critical rate, we model it as a random variable, use its distribution to calculate the decoding failure probability and formulate the expected distortion. We allocate a certain bit budget among different bitplanes such that the expected distortion is minimized. Moreover, we introduce fast decoding at the encoder, which helps us to estimate the critical rate far more accurately. Eventually, we achieve up to 1.5 dB gain in ratedistortion performance at high bit rates.

Transform Domain WynerZiv Video Coding with Refinement of Noise Residue and Side Information Distributed Video Coding (DVC) is a video coding paradigm which mainly exploits the source statistics at the decoder based on the availability of side information at the decoder. This paper considers feedback channel based Transform Domain WynerZiv (TDWZ) DVC. The coding efficiency of TDWZ video coding does not match that of conventional video coding yet, mainly due to the quality of side information and inaccurate noise estimation. In this context, a novel TDWZ video decoder with noise residue refinement (NRR) and side information refinement (SIR) is proposed. The proposed refinement schemes are successively updating the estimated noise residue for noise modeling and side information frame quality during decoding. Experimental results show that the proposed decoder can improve the RateDistortion (RD) performance of a stateoftheart WynerZiv video codec for the set of test sequences.