# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 2 Related Work
Instead of treating all features equally, the FMBlock smartly modulates features by discerning their frequency-domain characteristics. To achieve this, it harnesses the power of the discrete cosine transform (DCT), a tool known to capture frequency-based details. FMNet is trained on the same dataset used to train the previously described HDRTVNet, creating HDR images directly encoded with PQ-OETF and in the Rec.2020 color space. In [34], a CNN-based joint SR-ITM (super-resolution and inverse tone mapping) framework is presented. As with [20], this method focuses on avoiding color discrepancy and preserving long-range structural similarity in the image. This method proposes using a global priors-guided modulation network (GPGMNet). The main components of this network are the joint modulated residual modules (JMRMs), the global priors extraction module (GPEM), and the upsampling module. The GPEM extracts color conformity and structural similarity priors that are equally beneficial for SDRTV-to-HDRTV and SR tasks. The JMRM includes a spatial pyramid convolution block (SPCB) and a global priors-guided spatial-wise modulation block (GSMB). The SPCB provides multi-scale spatial features to the GSMB, which then modulates these using global priors and spatial information. Using a simplified global priors vector and consolidated spatial feature map, GPGMNet achieveseffective feature modulation with fewer computational needs than other methods. The final 4K HDR (PQ/BT.2020) image is created by adding the residual from the predicted image to an up-scaled image. In a recent study presented in [35], the authors introduce an SDR-to-HDRTV up-conversion technique. While most methods yield dim and desaturated outputs, this approach aims to produce HDR images that are not only accurate in reconstruction but also perceptually vibrant, enhancing the viewer's experience. To achieve this, the authors created a unique training set and new evaluation criteria grounded in perceptual quality estimation. The new dataset is used to train a luminance-segmented network (LSN) consisting of a global mapping trunk and two transformer branches on bright and dark luminance ranges. These works share a common goal with our research: to increase the dynamic range of LDR video sequences. However, because they often focus on individual frame processing, these techniques may induce temporal artifacts when the frames are stitched together into a video sequence. Additionally, they tend to introduce artifacts in oversaturated regions. Furthermore, they are generally limited to producing HDR content with peak brightness levels that match current HDR screens, typically around 1000 cd/m\({}^{2}\). Our study extends the existing research on inverse tone mapping of video sequences by introducing a complete LDR-to-HDR conversion pipeline specifically designed for this context. Our pipeline addresses the key limitations observed in previous works: it prevents the occurrence of artifacts using a simple expansion global operator, minimizes temporal artifacts by incorporating mechanisms for improved frame-to-frame stability, and supports peak brightness of up to 6000 cd/m\({}^{2}\), exceeding the typical 1000 cd/m\({}^{2}\) peak brightness of most approaches. In addition, its streamlined design facilitates real-time processing, a critical requirement for many practical applications.