## 3 Evaluation
### Elemental Image Compared to Viewpoint Image  
VPIs and EIs are two image structures that can be obtained from Holoscopic images. Previous research has shown significant results in estimating disparity maps utilising VPIs. VPIs can be created by extracting a single pixel from each EI and arranging them in a tiled manner. However, the process of extracting VPIs does not consistently provide ideal images, unlike the VPIs found in synthetic datasets and those obtained from Lytro (the camera's performance was hindered by extensive pre-processing, resulting in a slow performance). The production of these images involves a significant amount of pre-processing. Occasionally, these procedures may require choosing a group of pixels instead of just one, employing shift and integration techniques, and utilising other methodologies to remove artefacts. As depicted in Figure 13, the Holoscopic images used have undergone calibration and rectification, ensuring that the grid of the EIs aligns perfectly to extract the VPI images accurately. VPI images are extracted using traditional methods, obtaining one pixel per EI. Figure 14 displays three extracted VPIs from different locations. These images exhibit a lower clarity, higher noise, and a reduced resolution when compared to the images typically obtained from publicly available VPI datasets that have undergone extensive pre-processing. The difference in clarity between the EIs and VPIs can be seen in Figures 13 and 14. Figure 14: VPIs extracted from three different positions (0, 0), (50, 20), (68, 45). Figure 13: The Holoscopic image was calibrated and rectified, resulting in a total of \(68\times 45\) EIs, with each EI measuring \(74\times 74\) in size. Utilising pixel patches instead of single pixels to extract VPIs during pre-processing might lead to better outcomes, as demonstrated in Figure 15. However, increasing the size of the extracted patch leads to a decrease in angular information, as the number of VPIs obtained is dramatically reduced. The number of VPIs is directly related to the resolution of the EI, which represents the amount of angular information captured. Moreover, while examining Figure 15, it is apparent that the images require additional pre-processing to enhance the outcome. The process of obtaining VPIs also results in a substantial rise in the image generation time. This process can become particularly cumbersome when dealing with Holoscopic videos. The disparity map was obtained from the EIs of the dataset captured by the Brunel Holoscopic camera using our approach, and subsequently obtained from the extracted VPIs. The disparity maps obtained from VPIs are then transformed to generate EIs, enabling a comparison between EIs with direct disparity estimates and EIs with disparity estimations derived from VPIs, as seen in Figure 16. The closeup crops from the Holoscopic image reveal that the disparity calculated by the EIs is distinct and clear, whereas the EIs obtained from the disparity generated by the VPIs are distorted and ambiguous, as depicted in Figure 17.  
Figure 16: Disparity map derived from EIs and VPIs. (**a**) The disparity is calculated directly from the EIs using the raw Holoscopic image. Within the red-coloured box, there are a few extracted VPI disparities from the EI-based disparity. Their clarity is compromised by the low resolution. (**b**) VPIs are extracted from calibrated and rectified Holoscopic images, and the disparity map is obtained from them. These VPI disparities are then mapped back to EIs, allowing for a comparison between VPI-based and EI-based disparity maps. Figure 15: Holoscopic image, Spiderman: (\(64\times 34\) MLA) \(5160\times 2743\) and sample images from different VPIs retrieved using patch sizes ranging from \(5\times 5\) pixels to \(21\times 21\) pixels (p). As seen in the extracted VPIs, they still exhibit some artefacts. The disparity map, evaluated by the consistency check metric, utilises the entire raw Holoscopic image to optimise the efficiency and minimise the amount of time and effort required.