Video object error coding method based on compressive sensing

The recently emerged theory of compressive sensing (CS) has a remarkable result that signals having sparse representations in some known basis can be represented (with high probability) by taking a few random projection measurements of the signals. In this paper, we study some CS sparse reconstruction methods and propose a video object error coding method based on CS theory. The proposed system first assumes the moving objects have been segmented from background image and object-based motion compensated from the previous reconstruction frame, and then the resulting object error is encoded by using CS random matrix projection. Finally the coded measurements can be quantized to store or transmit. Experimental results demonstrate the object error blocks can be effectively recovered by using CS sparse reconstruction algorithms. This proposed method would be widely used in the object-based video compression fields.