Hdrtvformer: Efficient Sdrtv-to-Hdrtv via Affine Transformation and Spatial-Aware Transformer

Abstract

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.