# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation
### LDR-to-HDR Conversion: Objective Evaluation  
In our previous research presented in [13; 31; 32], we individually evaluated the two major components of our proposed pipeline: (i) the dynamic expansion method based on mid-level mapping and (ii) the false-contour removal method based on POCS. These assessments were conducted on static images. In this section, we shift our focus from static images to evaluating our inverse tone mapping pipeline on video sequences. For this objective evaluation, we used video content from our in-house dataset called the \"xDR Dataset\", which includes video sequences professionally captured and graded in HDR and LDR by the National Public-Service Broadcaster for the Flemish Region and Community of Belgium (VRT-Vlaamse Radio-en Televisieomroeporganisatie). The xDR Dataset includes ten video sequences with a resolution of 1920 \(\times\) 1080 at 25 frames per second. All sequences are color-graded in LDR and HDR. In particular, the HDR video sequences were natively color-graded using the SIM2 HDR47 ES6 MB display, which has a dynamic range of 17.5 stops and can reach a peak brightness of 6000 cd/m\({}^{2}\). HDR video sequences were stored in absolute luminance format with peak values reaching up to 6000 cd/m\({}^{2}\), making this dataset ideal for testing our pipeline. Figure 9 shows a preview of the video sequences in our test dataset, illustrating the variety of lighting conditions and contrast, ranging from deep shadows to extremely bright scenes. The xDR Dataset is publicly available at [[http://telin.ugent.be/~gluzardo/hdr-sdr-dataset/](http://telin.ugent.be/~gluzardo/hdr-sdr-dataset/)]([http://telin.ugent.be/~gluzardo/hdr-sdr-dataset/](http://telin.ugent.be/~gluzardo/hdr-sdr-dataset/)) (accessed on 19 April 2024). The LDR sequences in the test dataset were used as input to compute the HDR video sequences. We compared the HDR videos obtained from our proposed pipeline against the HDR videos obtained from deep SR-ITM (joint learning of super-resolution and inverse tone-mapping for 4K UHD HDR applications) [15] and FMNet (frequency-aware modulation network for SDR-to-HDR translation) [20]. To assess the quality of the HDR videos produced by each method, we employed the high dynamic range video quality metric (HDRVQM) [52], which estimates the quality of an input video sequence from 0 to 1, with higher values indicating better quality. One score is generated per sequence based on a video sequence used as reference. HDRVQM involves spatio-temporal analysis, which looks at both the spatial (how things appear in each frame) and temporal (how things change over time) aspects of a video. HDRVQM also considers \begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Specification** \\ \hline Computer model & Dell Precision 3660 \\ Processor & 13 th Gen Intel(R) Core(TM) i7-13700K 3.40 GHz \\ Graphic card & NVIDIA GeForce RTX 4600 Ti 16 GB \\ Operating System & Windows 11 \\ RAM & 64 GB \\ HDD & 2 TB NVMe SSD \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of the computer used for testing. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Steps 1/Timings (ms)** & **EO** & **EO + AS** & **EO + AS + DQ** & **EO + AS + DQ + HB** \\ \hline Mean & 3.36 & 10.84 & 12.96 & 15.93 \\ Standard deviation & 0.65 & 1.00 & 1.98 & 2.39 \\ Median & 3.10 & 10.65 & 12.18 & 14.97 \\ \hline \hline \end{tabular} \({}^{\dagger}\)EO: Dynamic range expansion, AS: artifact suppression, DQ: dequantization, and HB: highlight boosting.  
\end{table} 
Table 3: Frame timings of the different steps in our proposed processing pipeline. the human eye's fixation behavior, considering where and how people tend to focus when watching videos and allowing this metric to better align with actual human perception. We used HDRVQM to compare the HDR videos generated by each method (the \"distorted\" videos) against the ground truth HDR videos provided in the test dataset (the \"reference\" videos). HDRVQM was configured to match the format of the HDR files we were testing.