Since compressed video sequences may be corrupted or lost when transmitted over error-prone networks, error concealment techniques are very important for video communication. As a video sequence is a group of high-dimensional data, it can be considered as a big 3rd-order tensor. The methodologies that matricize high-dimensional data and then apply matrix-based method for further analysis often cause a loss of the internal structure information. Therefore, we built a tensor model to process such data, in order to preserve the natural multilinear structure. The key idea of our tensor model includes two parts. The first part is to construct a small tensor consist of the corrupted block and its several reference blocks. This part could be accomplished by block matching, but the traditional block matching might give a wrong match when the corrupted area is large and continuous. In order to overcome such situation, we proposed a flexible block matching scheme (FBM). The second part is to figure out the data in the corrupted part by tensor low rank approximation. Unlike the traditional low rank approximation, we did not fix the rank of core tensor as a constant number. Instead, the rank is flexible in our method, which is adaptive to the situation. Compared with some other error concealment method in the experiments, our method is able to achieve significantly higher PSNR (Peak Signal to Noise Ratio) as well as better visual quality.
['Zhiheng Zhou', 'Ming Dai', 'Ruzheng Zhao', 'Bo Li', 'Huiqiang Zhong', 'Yiming Wen']
Video error concealment scheme based on tensor model