Second-generation error concealment for video transport over error-prone channels

Abstract

Video transport over error-prone channels may result in loss or erroneous decoding of the video data. Error concealment is an effective mechanism to reconstruct the video data. In this paper, we review error-concealment methods and introduce a new framework, which we refer to as second-generation error concealment. All the error-concealment methods reconstruct the lost video data by making use of certain a priori knowledge about the video content. First-generation error concealment builds such a priori in a heuristic manner. The proposed second-generation error concealment builds the a priori by modeling the statistics of the video content explicitly, typically in the region of interest (ROI). Context-based models are trained with the correctly received video data and then used to replenish the lost video data. Trained models capture the statistics of the video content and thus reconstruct the lost video data better than reconstruction by heuristics. A new dynamic model 'updating principal components' (UPC) is proposed as a model for second-generation error concealment. UPC can be applied to pixel values to conceal loss of pixel data. In addition, UPC can be applied to motion vectors, which results in 'updating eigenflows' (U-Eigenflow), to conceal loss of motion vectors. With UPC applied to both pixel values and motion vectors, hybrid temporal/spatial error concealment can be achieved. The proposed second-generation error-concealment method provides superior performances to first-generation error-concealment methods. Copyright