Efficient compression plays a significant role in Light Field imaging technology because of the huge amount of data needed for their representation. Video encoders using different strategies are commonly used for Light Field image compression. In this paper, different video encoder implementations including HM, VTM, x265, xvc, VP9, and AV1 are analysed and compared in terms of coding efficiency, and encoder/decoder time-complexity. Light field images are compressed as pseudo-videos.
A layered light-field display is composed of several liquid crystal layers located in front of a backlight. The light rays emitted from the backlight intersect with different pixels on the layers depending on the outgoing directions. Therefore, this display can show multi-view images (a light field) in accordance with the viewing direction. This type of displays can also be used for head-mounted displays (HMDs) thanks to its dense angular resolution. The angular resolution is an important factor, because sufficiently dense angular resolution can provide accommodation cues, preventing visual discomfort caused by vergence accommodation conflict. To further enhance the angular resolution of a layered display, we propose to replace some of the layers with monochrome layers. While keeping the pixel size unchanged, our method can achieve three times higher resolution than baseline architecture in the horizontal direction. To obtain a set of color and monochrome layer patterns for a target light field, we developed two computation methods based on non-negative tensor factorization and a convolutional neural network, respectively.
In this paper we propose a solution for view synthesis of scenes presenting highly non-Lambertian objects. While Image- Based Rendering methods can easily render diffuse materials given only their depth, non-Lambertian objects present non-linear displacements of their features, characterized by curved lines in epipolar plane images. Hence, we propose to replace the depth maps used for rendering new viewpoints by a more complex “non- Lambertian map” describing the light field?s behavior. In a 4D light field, diffuse features are linearly displaced following their disparity, but non-Lambertian feature can follow any trajectory and need to be approximated by non-Lambertian maps. We compute those maps from nine input images using Bezier or polynomial interpolation. After the map computation, a classical Image- Based Rendering method is applied to warp the input images to novel viewpoints.
The estimated depth map provides valuable information in many computer vision applications such as autonomous driving, semantic segmentation and 3D object reconstruction. Since the light field camera capture both the spatial and angular light ray, we can estimate a depth map throughout that properties of light field image. However, estimating a depth map from the light field image has a limitation in term of short baseline and low resolution issues. Even though many approach have been developed, but they still have a clear flaw in computation cost and depth value accuracy. In this paper, we propose a network-based and epipolar plane image (EPI) light field depth estimation technique. Since the light field image consists of many sub-aperture images in a 2D spatial plane, we can stack the sub-aperture images in different directions to handle occlusion problem. However, usually used many light field subaperture images are not enough to construct huge datasets. To increase the number of sub-aperture images for stacking, we train the network with augmented light field datasets. In order to illustrate the effectiveness of our approach, we perform the extensive experimental evaluation through the synthetic and real light field scene. The experimental result outperforms the other depth estimation techniques.
Light field images are represented by capturing a densely projected rays from object to camera sensor. In this paper we propose a novel method for depth estimation from light field epipolar plane image (EPI). Contrary to the conventionally used depth estimation method such as stereo matching, the depth map is generated without high computational complexity via EPI. In order to obtain an accurate depth value from EPI, optimal angular value has to be founded for each pixels. If we consider all the angular candidate value for optimal angle value selection, that cause high computational complexity. Instead we consider all candidate value, reduce the angular candidate by analyzing the EPI patterns. In addition, to improve the quality of estimated depth map from EPI, occlusion area is handled before computing a matching cost. As a pre-processing, average and variance value are computed within specific window size to detect and replace the occlusion area. To validate the efficiency of our algorithm, we experiment with computer graphics and also dense light field data set. The experiment results show that our algorithm achieve better performance than conventionally used depth estimation methods.