# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 5 Conclusions and Future Work  
We have presented a comprehensive LDR-to-HDR pipeline for video sequences that incorporates several steps that effectively address common challenges in this field. Our proposed LDR-to-HDR pipeline can create HDR video sequences with 6000 cd/m\({}^{2}\) of peak brightness. It incorporates inverse tone mapping, artifact suppression, and highlight enhancement while adapting to the peak brightness of different displays. The experimental results show the real-time capability of our proposed pipeline. In addition, results show its ability to consistently generate HDR video sequences of superior quality compared to the original LDR inputs.  
Furthermore, our pipeline has the potential to significantly enhance the perceived quality of existing HDR content when increasing its peak brightness, especially relevant given that consumer-grade HDR content typically peaks at 1000 cd/m\({}^{2}\). This makes our pipeline a valuable tool for elevating the quality of HDR content beyond the displays it was originally intended for, enabling the content to be enjoyed on displays capable of higher brightness levels.  
While our pipeline usually results in video sequences perceived as higher quality than their inputs, further research is required. Future work will focus on refining the expansion mask estimation and ensuring its temporal stability to avoid temporal artifacts. Relative to other state-of-the-art methods, our pipeline demonstrates a promising capacity for brightness enhancement but requires refinement to effectively mitigate temporal artifacts produced during brightness enhancement.  
In our HDR-to-xHDR pipeline, we aimed to explore how different tone-mapping operators impact output quality and to investigate methods to bypass the use of a tone-mapped image, directly working with the HDR input by recalibrating the middle-output value estimation. These questions will be the focus of our ongoing research efforts. In addition, further comparative studies with existing state-of-the-art methods will be essential to benchmark our pipeline's performance, especially in terms of artifact presence and overall user satisfaction.  
This research was mainly conducted by [PERSON], [PERSON], [PERSON] and [PERSON]. The research was supervised by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported by Secretaria de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT) and Escuela Superior Politecnica del Litoral (ESPOL) under Ph.D. studies 2016. This work was supported by imec as part of the imec.ICON HD\({}^{2}\)R project.  
Not applicable.  
The **ADR** dataset used in this study is publicly available at [[http://telin.ugentbe/~gluzardo/hdr-sdr-dataset/](http://telin.ugentbe/~gluzardo/hdr-sdr-dataset/)]([http://telin.ugentbe/~gluzardo/hdr-sdr-dataset/](http://telin.ugentbe/~gluzardo/hdr-sdr-dataset/)) (accessed on 19 April 2024).  
We would like to thank the National Public-Service Broadcaster for the Flemish Region and Community of Belgium (VRT), especially [PERSON], for the support provided to create the video content that was used in training the dynamic range expansion operator within our pipeline.  
The authors declare no conflicts of interest.