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DCT statistics-based digital dropout detection in degraded archived media

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Abstract

With the rapid development of visual digital media, the demand for better quality of service has increased the pressure on broadcasters to automate their error detection and restoration activities for preserving their archives. Digital dropout is one of the defects that affect archived visual materials and tends to occur in block by block basis (size of 8 × 8). It is well established that human visual system (HVS) is highly adapted to the statistics of its visual natural environment. Consequently, in this paper, we have formulated digital dropout detection as a classification problem which predicts block label based on statistical features. These statistical features are indicative of perceptual quality relevant to human visual perception, and allow pristine images to be distinguished from distorted ones. Here, the idea is to extract discriminant block statistical features based on discrete cosine transform (DCT) coefficients and determine an optimal neighborhood sampling strategy to enhance the discrimination ability of block representation. Since this spatial frame based approach is free from any motion computation dependency, it works perfectly in the presence of fast moving objects. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.

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Acknowledgments

The authors wish to thank Korean Broadcasting System (KBS), South Korea for providing the video archives.

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Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggido, 446-701, South Korea

    Md. Monirul Hoque, Gihun Song, Kiok Ahn, Byungyong Ryu, Md. Tauhid Bin Iqbal & Oksam Chae

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  1. Md. Monirul Hoque
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  2. Gihun Song
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  3. Kiok Ahn
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  4. Byungyong Ryu
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  5. Md. Tauhid Bin Iqbal
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  6. Oksam Chae
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Correspondence to Oksam Chae.

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Hoque, M.M., Song, G., Ahn, K. et al. DCT statistics-based digital dropout detection in degraded archived media. Multimed Tools Appl 75, 4259–4283 (2016). https://doi.org/10.1007/s11042-015-2469-9

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  • DOI: https://doi.org/10.1007/s11042-015-2469-9

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