Abstract
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.
Highlights
Over the past few decades, transform-based coding has been widely used in lossy image and video compression to exploit the spatial correlation of visual signals
Proposed are more sophisticated methods that enhance the reconstructed video by using image/video restoration techniques such as iterative methods based on the theory of projection onto convex sets (POCS) or constrained minimization [14,15,16,17,18], maximum a posterior probability estimation approach (MAP) [19,20,21], and regularized image/video restoration [22,23,24,25,26,27]
By exploiting the information from the quantization constraint sets and transform coefficient statistics only in the transform domain, the proposed method can provide an enhanced video with better quality than any of the available copies while incurring much lower computational complexity compared with the previous method
Summary
Over the past few decades, transform-based coding has been widely used in lossy image and video compression to exploit the spatial correlation of visual signals. We address this new research problem by blindly enhancing the quality of the video reconstructed from multiple compressed copies of the same visual content, where existing postprocessing techniques may no longer be suitable nor effective as they usually consider only a single compressed video. By exploiting the information from the quantization constraint sets and transform coefficient statistics only in the transform domain, the proposed method can provide an enhanced video with better quality than any of the available copies while incurring much lower computational complexity compared with the previous method.
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