Distributed Video Coding (DVC) is a new paradigm in video coding, based on the Slepian-Wolf and Wyner-Ziv theorems. DVC offers a number of potential advantages: flexible partitioning of the complexity between the encoder and decoder, robustness to channel errors due to intrinsic joint source-channel coding, codec independent scalability, and multi-view coding without communications between the cameras. In this paper, we evaluate the performance of DVC in an error-prone wireless communication environment. We also present a hybrid spatial
and temporal error concealment approach for DVC. Finally, we perform a comparison with a state-of-the-art AVC/H.264 video coding scheme in the presence of transmission errors.
Error concealment is becoming increasingly important because of the growing interest in multimedia transmission over unreliable channels such as wireless channel. At present most concealment method has its own advantage as well as applicable limitation. In different case, it can achieve different concealment effect. In our paper, we present a novel feature-based image error detection and error concealment algorithm to improve the image quality which was degraded during its transmission over wireless channel. First a simulation channel based on Rayleigh mode is implemented to emulating the actual wireless fading channel characterized by fading, multipath and Doppler frequency shift. The damaged image blocks are detected by exploring the contextual information in images, such as their consistency and edge continuity. The statistical characteristics of missing blocks are then estimated based on the types of their surrounding blocks (e.g., smoothness, texture and edge). Finally different error concealment strategies are applied to different types of blocks in order to achieve better visual quality. Instead of assuming random errors in packets, we simulate the errors of wireless channel based on the Rayleigh model. The proposed algorithm is tested on a number of still images. Simulation results demonstrate that our proposed algorithm is effective in terms of visual quality assessment and PSNR.