Abstract
High dynamic range (HDR) UHD-TVs are being rapidly deployed in consumer markets, offering a highly realistic experience to customers. However, these HDR UHD-TVs still need to handle the legacy low resolution (LR) video of standard dynamic range (SDR). In this paper, we propose a convolutional neural network based structure for the joint learning of super-resolution and inverse tone-mapping, which can be used for converting LR-SDR legacy video to high resolution (HR) HDR video. Our proposed structure is designed to perform three tasks: (i) SDR-to-HDR conversion of LR images, (ii) super-resolution of LR-SDR images to HR-SDR images and (iii) joint conversion from LR-SDR to HR-HDR images. We show the effectiveness of our proposed joint learning CNN architecture with extensive experiments.
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00419, Intelligent High Realistic Visual Processing for Smart Broadcasting Media).
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Kim, S.Y., Kim, M. (2019). A Multi-purpose Convolutional Neural Network for Simultaneous Super-Resolution and High Dynamic Range Image Reconstruction. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_24
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