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Automatic selection of color reference image for panoramic stitching

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Abstract

Color correction is an important step in the generation of high-resolution stitched panoramas. Typically, color correction schemes try to match the color of each image in the panorama to an arbitrarily selected reference image. We provide a scheme that uses quantitative metrics such as image contrast, gradient-based structure similarity index measure (G-SSIM) and color clipping information to automatically select the best reference that results in visually pleasing output panoramas. Quantitative and qualitative evaluation of the scheme show encouraging results for panoramic images as well as for stitched videos. The scheme acts as a pre-processing step to color correction and its applicability to both parametric and non-parametric global color correction schemes has also been demonstrated.

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Acknowledgments

We would like to thank IT R&D program of MKE/ETRI (14ZR1110, HCI-based UHD Panorama Technology Development) for their generous funding. NUST authors are also thankful to National ICT R&D Fund Pakistan for their support. 

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Authors and Affiliations

  1. Vision Image and Signal Processing (VISpro) Lab, School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan

    Muhammad Twaha Ibrahim, Rehan Hafiz & Muhammad Murtaza Khan

  2. Broadcasting and Telecommunications Media Research Laboratory, Electronics and Telecommunications Research Institute, ETRI, Daejeon, Republic of Korea

    Yongju Cho

  3. Faculty of Computer Science and Information Technology, University of Jeddah, Jeddah, Kingdom of Saudi Arabia

    Muhammad Murtaza Khan

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  1. Muhammad Twaha Ibrahim
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  2. Rehan Hafiz
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  3. Muhammad Murtaza Khan
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  4. Yongju Cho
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Corresponding author

Correspondence to Rehan Hafiz.

Additional information

Communicated by L. Zhang.

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Ibrahim, M.T., Hafiz, R., Khan, M.M. et al. Automatic selection of color reference image for panoramic stitching. Multimedia Systems 22, 379–392 (2016). https://doi.org/10.1007/s00530-015-0467-4

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