Low Bit-rate Compression of Video and Light-field Data using Coded Snapshots and Learned Dictionaries

Ajit Rajwade

Abstract:

The method of coded snapshots has been proposed recently
for compressive acquisition of video data to overcome the
space-time trade-of inherent in video acquisition. The method
involves modulation of the light entering the video camera at
different time instants during the exposure period by means
of a different and randomly generated code pattern at each
of those time instants, followed by integration across time,
leading to a single coded snapshot image. Given this image
and knowledge of the random codes, it is possible to reconstruct the underlying video frames - by means of sparse coding on a suitably learned dictionary. In this paper, we apply a modified version of this idea, proposed formerly in the
compressive sensing literature, to the task of compression of
videos and light-field data. At low bit rates, we demonstrate
markedly better reconstruction fidelity for the same storage
costs, in comparison to JPEG2000 and MPEG-4 (H.264) on
light-field and video data respectively. Our technique can
cope with overlapping blocks of image data, thereby leading
to suppression of block artifacts.