Light field imaging is characterized by capturing brightness, color, and
directional information of light rays in a scene. This leads to image
representations with huge amount of data that require efficient coding schemes.
In this paper, lenslet images are rendered into sub-aperture images. These
images are organized as a pseudo-sequence input for the HEVC video codec. To
better exploit redundancy among the neighboring sub-aperture images and
consequently decrease the distances between a sub-aperture image and its
references used for prediction, sub-aperture images are divided into four
smaller groups that are scanned in a serpentine order. The most central
sub-aperture image, which has the highest similarity to all the other images,
is used as the initial reference image for each of the four regions.
Furthermore, a structure is defined that selects spatially adjacent
sub-aperture images as prediction references with the highest similarity to the
current image. In this way, encoding efficiency increases, and furthermore it
leads to a higher similarity among the co-located Coding Three Units (CTUs).
The similarities among the co-located CTUs are exploited to predict Coding Unit
depths.Moreover, independent encoding of each group division enables parallel
processing, that along with the proposed coding unit depth prediction decrease
the encoding execution time by almost 80% on average. Simulation results show
that Rate-Distortion performance of the proposed method has higher compression
gain than the other state-of-the-art lenslet compression methods with lower
computational complexity