Distributed video coding based on constrained rate adaptive low density parity check Codes Distributed video coding is an emerging area of research for digital video driven partly by the widespread use of video acquisition devices by the consumers. In this paper, we present a distributed video coding scheme based on zero motion identification at the decoder and constrained rate adaptive low density parity check (LDPC) codes. Zeromotionblock identification mechanism is introduced at the decoder, which takes the characters of video sequence into account. The constrained error control decoder can use the bits in the zero motion blocks as a constraint to achieve a better decoding performance and further improve the overall video compression efficiency. It is only at the decoder side that the proposed scheme exploits temporal and spatial redundancy without introducing any additional processing at the encoder side, which keeps the complexity of the encoding as low as possible with certain compression efficiency. As a powerful alternative to Turbo codes, LDPC codes have been applied to our scheme. Since video data are highly nonergodic, we use rateadaptive LDPC codes to fit this variation of the achievable compression rate in our scheme. But one most basic difference between LDPC codes in our scheme and conventional channel coding is that in our scheme, we can make sure some bits are known. Those bits can work as constraints to the decoder and improve the decoding performance. We propose a constrained LDPC decoder not only to improve the decoder efficiency but also to speed the convergence of the iterative decoding. Simulation demonstrates that the scheme has significant improvement in the performances. In addition, the proposed constrained LDPC decoder may benefit other application.

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.