This dissertation includes a collection of studies that aim to improve the way we represent and visualize volume data. The advancement of medical imaging has revolutionized healthcare, providing crucial anatomical insights for accurate diagnosis and treatment planning. Our first study introduces an innovative technique to enhance the utility of medical images, transitioning from monochromatic scans to vivid 3D representations. It presents a framework for reference-based automatic color transfer, establishing deep semantic correspondences between a colored reference image and grayscale medical scans. This methodology extends to volumetric rendering, eliminating the need for manual intervention in parameter tuning. Next, it delves into deep learning-based super-resolution for volume data. By leveraging color information and supplementary features, the proposed system efficiently upscales low-resolution renderings to achieve higher fidelity results. Temporal reprojection further strengthens stability in volumetric rendering. The third contribution centers on the compression and representation of volumetric data, leveraging coordinate-based networks and multi-resolution hash encoding. This approach demonstrates superior compression quality and training efficiency compared to other state-of-the-art neural volume representation techniques. Furthermore, we introduce a meta-learning technique for weight initialization to expedite convergence during training. These findings collectively underscore the potential for transformative advancements in large-scale data visualization and related applications.