Video Enhancement from Multiple Compressed Copies in Transform Domain

Increasingly, we can obtain more than one compressed copy of the same video content with different levels of visual quality over the Internet. As the original source video is not always available, how to choose or derive a video of the best quality from these copies becomes a challenging and interesting problem. In this paper, we address this new research problem by blindly enhancing the quality of the video reconstructed from such multiple compressed copies. The aim is to reconstruct a video that achieves a better quality than any of the available copies. Specifically, we propose to reconstruct each coefficient of the video in the transform domain by using a narrow quantization constraint set derived from the multiple compressed copies together, using a Laplacian or Cauchy distribution model for each AC transform coefficient to minimize the distortion. Analytical and experimental results show the effectiveness of the proposed method

Video Enhancement from Multiple Compressed Copies in Transform Domain

Increasingly, we can obtain more than one compressed copy of the same video content with different levels of visual quality over the Internet. As the original source video is not always available, how to choose or derive a video of the best quality from these copies becomes a challenging and interesting problem. In this paper, we address this new research problem by blindly enhancing the quality of the video reconstructed from such multiple compressed copies. The aim is to reconstruct a video that achieves a better quality than any of the available copies. Specifically, we propose to reconstruct each coefficient of the video in the transform domain by using a narrow quantization constraint set derived from the multiple compressed copies together, using a Laplacian or Cauchy distribution model for each AC transform coefficient to minimize the distortion. Analytical and experimental results show the effectiveness of the proposed method.</p