Error concealment at a decoder is an efficient method to reduce the degradation of visual quality caused by channel errors. In this paper, we propose a novel spatio-temporal error concealment algorithm based on the spatial-temporal fading (STF) scheme which has been recently introduced. Although STF achieves good performance for the error concealment, several drawbacks including blurring still remain in the concealed blocks. To alleviate these drawbacks, in the proposed method, hybrid approaches with adaptive weights are proposed. First, the boundary matching algorithm and the decoder motion vector estimation which are well-known temporal error concealment methods are adaptively combined to compensate for the defect of each other. Then, an edge preserved method is utilized to reduce the blurring effects caused by the bilinear interpolation for spatial error concealment. Finally, two concealed results obtained by the hybrid spatial and temporal error concealment are pixel-wisely blended with adaptive weights. Experimental results exhibit that the proposed method outperforms conventional methods including STF in terms of the PSNR performance as well as subjective visual quality, and the computational complexity of the proposed method is similar to that of STF.

In low bit-rate video transmission, the payload of a single packet can often contain a whole coded frame due to the high compression ratio in both spatial and temporal domains. Thus, the loss of a single packet can lead to the loss of a whole video frame. In this paper, we propose a novel error concealment algorithm that can effectively reconstruct the lost frame and protect the quality of video streams from the degradation caused by propagation errors. The proposed algorithm employs a bilateral motion estimation scheme where the weighted sum of the received motion vectors (MVs) in the neighboring frames is utilized to construct the MV field for the concealed frame. Unlike the conventional algorithms, the proposed scheme does not produce any overlapped pixel and hole region in the reconstructed frame. The proposed algorithm can be applied not only to the case of single frame loss but also adaptively extended to the case of multiframe loss. Experimental results show that the proposed algorithm outperforms other conventional techniques in terms of both peak signal-to-noise ratio (PSNR) performance and subjective visual quality.