An event camera adopts a bio-inspired sensing mechanism that can record the luminance changes over time. The recorded information, called events, are detected asynchronously at each pixel in the order of microseconds. Events are quite useful for framerate upsampling of a video, because the information between the low-framerate video frames (key-frames) can be supplemented from the events. We propose a method for framerate upsampling from events on the basis of an unsupervised approach; our method does not require ground-truth high-framerate videos for pre-training but can be trained solely on the key-frames and events taken from the target scene. We also report some promising experimental results with a fast moving scene captured by a DAVIS346 event camera.
KEYWORDS: Education and training, Quantization, Network architectures, Data compression, Video coding, Neural networks, Visualization, Image compression, 3D acquisition, Video
We propose a data compression method for a light field using a compact and computationally efficient neural representation. We first train a neural network with learnable parameters to reproduce the target light field. We then compress the set of learned parameters as an alternative representation of the light field. Our method is significantly different in concept from the traditional approaches where a light field is encoded as a set of images or a video (as a pseudo-temporal sequence) using off-the-shelf image/video codecs. We experimentally show that our method achieves a promising rate-distortion performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.