Video Inverse Tone Mapping Network with Luma and Chroma Mapping

With the popularity of consumer high dynamic range (HDR) display devices, video inverse tone mapping (iTM) has become a research hotspot. However, existing methods are designed based on a perceptual non-uniformity color space (e.g., RGB and YC_BC_R), resulting in limited quality of HDR video rendered by these methods. Considering the two key factors involved in the video iTM task: luma and chroma, in this paper, we design an IC_TC_P color space based video iTM model, which reproduces high quality HDR video by processing luma and chroma information. Benefitting from the decorrelated perception of luma and chroma in the IC_TC_P color space, two global mapping networks (INet and TPNet) are developed to enhance the luma and chroma pixels, respectively. However, luma and chroma mapping in the iTM task may be affected by color appearance phenomena. Thus, a luma-chroma adaptation transform network (LCATNet) is proposed to process the luma and chroma pixels affected by color appearance phenomena, which can complement the local details to the globally enhanced luma and chroma pixels. In the LCATNet, either the luma mapping or the chroma mapping is adaptively adjusted according to both the luma and the chroma information. Besides, benefitting from the perceptually consistent property of the IC_T C_P color space, the same pixel errors can draw equal model attentions during the training. Thus, the proposed model can correctly render luma and chroma information without highlighting special regions or designing special training losses. Extensive experimental results demonstrate the effectiveness of the proposed model.