# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
While HDR content is gaining popularity, it remains a relatively recent advancement in both video acquisition and display technology. Therefore, not only is historical media content only available in LDR, a large amount of legacy LDR content is still consumed. As HDR becomes more prevalent, the proportion of HDR content will increase, but for now, the vast bulk of media archives worldwide remain in LDR. To display LDR content accurately on HDR screens, it must undergo a process known as inverse tone mapping (iTM), which converts LDR content to HDR (up-conversion) [1]. Traditionally, iTM methods have been designed to create HDR images with the best subjective quality when viewed on HDR screens. These methods vary in complexity and approach, ranging from simple techniques that retain key attributes of the LDR content such as contrast and color [8; 9; 10; 11; 12; 13; 14], to more complex machine learning-based techniques [15; 16; 17; 18; 19; 20; 21]. In addition, other LDR-to-HDR conversion methods, known as single-image HDR reconstruction (SI-HDR) methods, have recently emerged. Unlike conventional inverse tone mapping methods, SI-HDR methods work on estimating physical light quantities and reconstructing missing details in overexposed or saturated regions [22; 23; 24; 25; 26; 27; 28; 29; 30]. Despite advancements, LDR-to-HDR conversion encounters the following challenges:  
* Noise artifact boosting: Noise artifacts are ubiquitous in almost all LDR content. However, due to the low contrast and luminance levels of LDR displays, these are often imperceptible to the human eye. However, when this content is expanded to the HDR domain and displayed on an HDR screen, these artifacts can become noticeable. * False contouring: This issue refers to the creation or emphasis of artifacts known as false contours. In HDR images, these contours emerge due to the coarse quantization of smooth regions into finite codewords, where a significant number of codewords are required to achieve a gradual color transition in the HDR domain [31]. * Lack of detail in under/overexposed regions: When a scene is captured in LDR, it may not fully represent areas with extremely bright or dark elements, leading to a loss of intricate detail. This issue can stem from two distinct causes: the technological limitations of the camera sensor in capturing the full spectrum of luminance values and intentional artistic grading choices that aim to suppress certain parts of the dynamic range for aesthetic purposes. For instance, in a high-contrast scene, details in very bright regions (overexposed) or very dark regions (underexposed) might not be fully captured due to the sensor's dynamic range limitation. Simultaneously, during the grading process, deliberate enhancement or reduction of these details occurs, aligning with the artistic vision within the constraints of a limited dynamic range. The challenge for inverse tone mapping methods lies in reconstructing the lost details in these regions, details such as subtle textures, shadow gradients, or highlights not captured in the original LDR image. * Desaturated color appearance: Saturation in images refers to the intensity or purity of colors. Highly saturated colors appear vivid and intense, while desaturated colors appear muted and dull. In the context of inverse tone mapping, the problem arises when the color intensity of certain elements in the resulting inverse tone mapped HDR image appears less vibrant or vivid compared to the original LDR content. It has been observed that desaturation not only depends on the expansion function used in inverse tone mapping methods but also on the colorfulness of the input LDR content [12]. * Temporal artifacts: This problem refers to visual inconsistencies or irregularities that occur over time when applying inverse tone mapping methods to video sequences. These issues stem from the fact that each frame is processed independently, without any reference to the previous frame's state. This can lead to noticeable disruptionsin the appearance of the video.