# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 2 Related Work  
While several methods for inverse tone mapping have been developed, many are optimized for images. Extending these techniques to video sequences introduces distinct challenges, often resulting in troublesome temporal artifacts. In particular, learning-based methods have been found to produce noticeable artifacts, especially in overexposed areas. Moreover, the appearance of these artifacts varies significantly between consecutive frames within a video sequence [23]. In this section, we explore more recent LDR-to-HDR approaches specifically tailored to video sequences. In [11], one of the first inverse tone mapping methods for video sequences is presented. This approach is oriented to managing situations where input images contain extensive overexposed regions, allowing for the creation of high-quality HDR images and videos across various exposure conditions. The method involves the computation of an \"expand map\" derived from the input LDR image. This map identifies areas where image information may have been lost. These regions are meticulously filled using a smooth function to avoid the creation of artifacts. Notably, the authors propose a cross-bilateral filtering technique as a smooth function; the results demonstrate its ability to generate smooth gradients while preserving intricate details from the original image. In [15], an inverse tone mapping method based on deep convolutional networks (CNN) known as deep SR-ITM (super-resolution inverse tone mapping) is introduced. Unlike other CNN-based methods [22; 23], what sets this method apart is its ability to restore lost details across the entire image, not just within overexposed regions. Significantly, deep SR-ITM is trained using 4K-PQ-encoded videos in the Rec.2020 (BT.2020) color space. This training allows it to produce HDR video frames directly encoded in PQ-OETF, making this method practical for HDR TV applications. Moreover, deep SR-ITM can upscale the resolution of an input LDR image to 4K-UHD (3840 \(\times\) 2160 pixels resolution) while concurrently expanding its dynamic range. This method uses a so-called \"Joint Convolutional Neural Network\" designed to both enhance the dynamic range (through inverse tone mapping) and retrieve lost fine details (via super-resolution). In [16], JSI-GAN, a method based on generative adversarial networks (GAN), is presented. Similar to SR-ITM, it can increase the resolution of the LDR input image/frame during the inverse tone mapping operation. JSI-GAN is trained on the same data as deep SR-ITM, and it also produces HDR images/frames directly encoded in PQ-OETF in the Rec.2020 color space. JSI-GAN comprises three task-specific sub-nets, each serving a unique purpose: (i) a detail restoration sub-net, (ii) a local contrast enhancement sub-net, and (iii) an image reconstruction sub-net. HDRTVNet, a pipeline to create HDR content from LDR content for TV, is presented in [17]. This three-step pipeline solution includes adaptive global color mapping, local enhancement, and highlight generation. The adaptive global color mapping converts the input LDR content into the HDR domain. Local enhancement acts as a refining process, increasing the local contrast of the resulting HDR image using local information. Highlight generation focuses on the overexposed areas of the image to restore any information that may have been lost. Each step in the pipeline is performed using CNNs, and as in previous methods, the resulting HDR image is encoded with PQ-OETF in the Rec.2020 color space. A refined iteration, HDRTVNet++, is presented in [33], offering enhanced restoration accuracy. In [20], a novel approach for converting LDR to HDR conversion is presented by introducing the frequency-aware modulation network (FMNet). Traditional CNN techniques often face challenges with artifacts appearing in low-frequency regions. FMNet addresses this limitation by including a frequency-aware modulation block (FMBlock).