Abstract
In this paper, a novel digital video resolution enhancement algorithm based on adaptive directional interpolation is proposed, where the directionality of the edge structure and the nonlocal self-similarity prior within the current frame as well as its adjacent frames are both considered. First, we establish the regularization equation that conforms to the prior model of a video frame and then take the classic bicubic interpolation result as the initial estimation to iteratively solve the restoration equation, in which the edge structures and contours in low resolution (LR) input are reconstructed to estimate and refine the desired high resolution (HR) output. Experimental results show that the proposed algorithm can effectively enhance the clarity of a video frame, with satisfying subjective visual quality and PSNR value.
Highlights
Videos and images are the main sources of information for humans
The image quality of digital video has been desired higher and higher, where the clarity index comes from standard definition to high definition (HD) and ultrahigh definition, as well as the corresponding resolution index comes from 480p to 720p, 1080p, and 2160p (4K)
We first establish the regularization equation that conforms to the prior model of a video frame and take the classic bicubic interpolation result as the initial estimation to iteratively solve the restoration equation, where the edge structures and contours in low resolution (LR) input are reconstructed to estimate and refine the desired high resolution (HR) output
Summary
Videos and images are the main sources of information for humans. According to statistics, more than 80% of the information we receive from the outside world comes from vision. These improvements in clarity and resolution can meet the increasing demand of end users and provide better image quality; on the other hand, while highresolution video provides more details in content, it adds burdens to the entire production and consumption ecosystem: more expensive capture and storage devices on the image acquisition side, additional computing resource requirement for video editing on the media creation side, and more data transmission pressure on the communication network side. In order to solve this problem, a common way is to use an image postprocessing procedure where the LR input frame is interpolated by a superresolution method [1,2,3,4,5,6,7], leading to a resolution-enhanced HR one This software-based technique does not change the existing image acquisition and data transmission systems and is of great value in fields of videotelephony, virtual reality, augmented reality, and HD video games.
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