Abstract
Recent years have witnessed a growing popularity of 4K or ultra high definition (UHD) content. However, the acquisition, production, post-production, and distribution pipelines of such content often go through stages where the actual video resolution goes below 4K/UHD level and is then upscaled to 4K/UHD resolution at later stages. As a result, the claimed 4K content in the real world often drops below the intended 4K quality, while final consumers are not well informed about such quality degradation. Here, we present our recent research progress on automatic image resolution assessment methods that determine whether a given image has true 4K resolution or not. Specifically, we developed a largest of its kind database of more than 10,000 true and fake 4K/UHD images with ground-truth labels. We have also made some initial attempts on constructing edge feature, Fourier transform feature, and deep learning based methods for the classification task. We believe that the built database and the attempted methods will help accelerate the research progress on automatic image resolution assessment.
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Akundy, V.A., Wang, Z. (2020). 4K or Not? - Automatic Image Resolution Assessment. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_6
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DOI: https://doi.org/10.1007/978-3-030-50347-5_6
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