Multiview images are obtained by recording a scene from different viewpoints. The
additional information can be used to improve the performance of various applications
ranging from e-commerce to security surveillance. Many such applications process large
arrays of images, and therefore it is important to consider how the information is stored
and transmitted.
In this thesis we address the issue of multiview image compression. Our approach
is based on the concept that a point in a 3D space maps to a constant intensity line in
specific multiview image arrays. We use this property to develop a sparse representation
of multiview images. To obtain the representation we segment the data into layers,
where each layer is defined by an object located at a constant depth in the scene. We
extract the layers by initialising the layer contours and then by iteratively evolving them
in the direction which minimises an appropriate cost function. To obtain the sparse
representation we reduce the redundancy of each layer by using a multi-dimensional
discrete wavelet transform (DWT). We apply the DWT in a separable approach; first
across the camera viewpoint dimensions, followed by a 2D DWT applied to the spatial
dimensions. The camera viewpoint DWT is modified to take into account the structure
of each layer, and also the occluded regions.
Based on the sparse representation, we propose two compression algorithms. The
first is a centralised approach, which achieves a high compression, however requires the
transmission of all the data. The second is an interactive method, which trades-off
compression performance in order to facilitate random access to the multiview image
dataset. In addition, we address the issue of rate allocation between encoding of the layer contours and the texture. We demonstrate that the proposed centralised and
interactive methods outperform H.264/MVC and JPEG 2000, respectively.