IEEE Trans Image Process. 2014 Jul;23(7):2804-19. doi: 10.1109/TIP.2014.2320364. Epub 2014 Apr 25.
Transform domain Wyner-Ziv (TDWZ) video coding is an efficient approach to distributed video coding (DVC), which provides low complexity encoding by exploiting the source statistics at the decoder side. The DVC coding efficiency depends mainly on side information and noise modeling. This paper proposes a motion re-estimation technique based on optical flow to improve side information and noise residual frames by taking partially decoded information into account. To improve noise modeling, a noise residual motion re-estimation technique is proposed. Residual motion compensation with motion updating is used to estimate a current residue based on previously decoded frames and correlation between estimated side information frames. In addition, a generalized reconstruction algorithm to optimize a multihypothesis reconstruction is proposed. The proposed techniques using motion and reconstruction re-estimation (MORE) are integrated in the SING TDWZ codec, which uses side information and noise learning. For Wyner-Ziv frames using GOP size 2, the MORE codec significantly improves the TDWZ coding efficiency with an average (Bjøntegaard) PSNR improvement of 2.5 dB and up to 6 dB improvement compared with DISCOVER.
变换域 Wyner-Ziv（TDWZ）视频编码是一种有效的分布式视频编码（DVC）方法，它通过在解码器侧利用源统计信息来提供低复杂度的编码。DVC 编码效率主要取决于侧信息和噪声建模。本文提出了一种基于光流的运动重估计技术，通过考虑部分解码信息来改进侧信息和噪声残差帧。为了改进噪声建模，提出了一种噪声残差运动重估计技术。使用运动更新的残差运动补偿根据先前解码的帧和估计的侧信息帧之间的相关性来估计当前残差。此外，还提出了一种广义重构算法来优化多假设重构。所提出的使用运动和重构重估计（MORE）的技术集成在 SING TDWZ 编解码器中，该编解码器使用侧信息和噪声学习。对于使用 GOP 大小为 2 的 Wyner-Ziv 帧，MORE 编解码器显著提高了 TDWZ 编码效率，与 DISCOVER 相比，平均（Bjøntegaard）PSNR 提高了 2.5dB，最高提高了 6dB。
