EffiHDR: An Efficient Framework for HDRTV Reconstruction and Enhancement in UHD Systems

Abstract

Recent advancements in SDRTV-to-HDRTV conversion have yielded impressive results in reconstructing high dynamic range television (HDRTV) videos from standard dynamic range television (SDRTV) videos. However, the practical applications of these techniques are limited for ultra-high definition (UHD) video systems due to their high computational and memory costs. In this paper, we propose EffiHDR, an efficient framework primarily operating in the downsampled space, effectively reducing the computational and memory demands. Our framework comprises a real-time SDRTV-to-HDRTV Reconstruction model and a plug-and-play HDRTV Enhancement model. The SDRTV-to-HDRTV Reconstruction model learns affine transformation coefficients instead of directly predicting output pixels to preserve high-frequency information and mitigate information loss caused by downsampling. It decomposes SDRTV-to-HDR mapping into pixel intensity-dependent and local-dependent affine transformations. The pixel intensity-dependent transformation leverages global contexts and pixel intensity conditions to transform SDRTV pixels to the HDRTV domain. The local-dependent transformation predicts affine coefficients based on local contexts, further enhancing dynamic range, local contrast, and color tone. Additionally, we introduce a plug-and-play HDRTV Enhancement model based on an efficient Transformer-based U-net, which enhances luminance and color details in challenging recovery scenarios. Experimental results demonstrate that our SDRTV-to-HDRTV Reconstruction model achieves real-time 4K conversion with impressive performance. When combined with the HDRTV Enhancement model, our approach outperforms state-of-the-art methods in performance and efficiency.