Abstract
The light field image, also known as the plenoptic image, contains the information about not only the intensity of light in a scene but also the direction of the light rays in space. Since the light field image contains very rich photometric and geometric information, it will have very widespread application in the future. For example, immersive content capture for virtual and mixed reality presentation or depth from light field for auto driving applications. To be more specific, a light field image can be enhanced with physical models for an autonomous decision making process, which is also an important task of the Dynamic Data Driven Applications Systems (DDDAS) [11]. Besides, the rich geometry and photometric information contained in the light field can be updated with real-time measurements, which is a focus of DDDAS such as smart city related image and video processing tasks. However, to make the light field images easier to be utilized, one of the most important tasks is to compress the light field images efficiently so they can be easily distributed over the current communication infrastructure.
Highlights
Light field imaging based on a single-tier camera equipped with a microlens array has currently risen up as a practical and prospective approach for future visual applications and services
Light field imaging based on a single-tier camera equipped with a microlens array (MLA) – referred to as Light Field (LF) in this chapter – has currently risen up as a practical and prospective approach for future visual applications and services
The results show that, by exploiting redundancies in the spatial and view angle domain, the High Efficiency Video Coding (HEVC) encoding tools are more efficient than JPEG exploiting only spatial redundancies in the whole LF image
Summary
Light field imaging based on a single-tier camera equipped with a microlens array (MLA) – referred to as Light Field (LF) in this chapter – has currently risen up as a practical and prospective approach for future visual applications and services. Successfully deploying actual LF imaging applications and services will require identifying adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, this chapter overviews some relevant LF image coding solutions that have been recently proposed in the literature. This coding scheme is scalable with three layers such that the rendering can be performed with the sparse MI set, the reconstructed LF image, and the decoded LF image. It is shown that this coding scheme improves considerably the coding efficiency with respect to HEVC Intra and is slightly better than the spatial displacement compensated prediction with multiple hypotheses
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