Hdrtvformer: Efficient Sdrtv-to-Hdrtv via Affine Transformation and Spatial-Aware Transformer
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2024)
摘要
Recent works on reconstructing HDR videos in display format (HDRTV) suffer from high computational and memory requirements because they learn the SDRTV-to-HDRTV mapping directly in 4K resolution. This paper proposes an efficient SDRTV-to-HDRTV model (HDRTVFormer) that decomposes the HDRTV restoration into SDRTV-to-HDRTV Domain Mapping and HDRTV Refinement. SDRTV-to-HDRTV Domain Mapping is an affine transformation-based model that learns SDRTV-to-HDRTV affine coefficients in low-resolution space, achieving rapid processing times. To enhance the accuracy of the predicted affine coefficients, the model introduces global information-modulated feature extraction blocks and a detail guidance upsampling module. For HDRTV Refinement, we propose a spatial-aware Transformer to refine the luminance and color details. We modify the self-attention and feed-forward network of Transformer blocks to improve efficiency and feature representations. Experimental results have demonstrated that our method outperforms other state-of-the-art works in performance and efficiency.
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关键词
Efficient SDRTV-to-HDRTV,Affine transformation,Multi-scale,Transformer
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