Abstract
In this paper we address the problem of how to identify real 4K video contents from pseudo ones which were generated using complicated image post-processing technologies such as upsampling or super-resolution (SR). Two individual sets of 22 4K video sequences and 6 popular SR algorithms were collected for conducting this research. Despite the superior performance of current SR methods, they still not reach the perfect level since SR image reconstruction is a severely ill-posed problem and details are hard to be entirely recovered. This implies the reconstructed images based on SR methods do not conform to the natural scene statistics (NSS) model. According to this, we explore the difference of the locally normalized luminances’ distributions between real 4K video sequences and SR-generated pseudo 4K video sequences. It was shown by extensive tests that simple statistical features can be fused to form a good classifier for separating real 4K video contents from pseudo versions. Experimental results demonstrate that the proposed classier consumes less than a quarter of a second for a 4K video frame and the accuracy of classification is beyond 90%. abstract environment.
M. Liu—This work was supported in part by National Natural Science Foundation of China under Grants 61533002 and 61703009.
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Notes
- 1.
More advanced upsampling or SR methods will be discussed later.
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Liu, M., Qiao, J., Wu, L., Zhang, H. (2018). How to Efficiently Identify Real and Pseudo 4K Video Contents?. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_13
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