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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Mavridaki, E., Mezaris, V.: No-reference blur assessment in natural images using Fourier transform and spatial pyramids. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 566–570 (2014)
Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–3364 (2011)
Sheikh, H.R., Bovik, A.C., Cormack, L.: No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Trans. Image Process. 14(11), 1918–1927 (2005)
Bayar, B., Stamm, M.C.: On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2152–2156 (2017)
Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 5–10 (2016)
Shrivakshan, G.T., Chandrasekar, C.: A comparison of various edge detection techniques used in image processing. Int. J. Comput. Sci. Issues (IJCSI) 9(5), 269–276 (2012)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015)
Nilsback, M.-E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-50347-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50346-8
Online ISBN: 978-3-030-50347-5
eBook Packages: Computer ScienceComputer Science (R0)