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Authors
Abstract(s)
This dissertation describes the research and development of some techniques to enhance
the disparity compensation in 3D video compression algorithms. Disparity compensation
is usually performed using a block matching technique between views, disregarding the
various levels of disparity present for objects at different depths in the scene. An alternative
coding scheme is proposed, taking advantage of the cameras setup information and
the object’s depth in the scene, to compensate more complex spatial distortions, being
able to improve disparity compensation even with convergent cameras.
In order to perform a more accurate disparity compensation, the reference picture
list is enriched with additional geometrically transformed images, for the most relevant
object’s levels of depth in the scene, resulting from projections of one view to another.
This scheme can be implemented in any state-of-the-art video codec, as H.264/AVC or
HEVC, in order to improve the disparity matching accuracy between views.
Experimental results, using MV-HEVC extension, show the efficiency of the proposed
method for coding stereo video, presenting bitrate savings up to 2.87%, for convergent
camera sequences, and 1.52% for parallel camera sequences. Also a method to choose
the geometrically transformed inter view reference pictures was developed, in order to
reduce unnecessary overhead for unused reference pictures. By selecting and adding to
the reference picture list, only the most useful pictures, all results improved, presenting
bitrate savings up to 3.06% for convergent camera sequences, and 2% for parallel camera
sequences.
Description
Keywords
Disparity compensation 3d video coding Geometric transforms