Statistical representation and coding of light field data
A method of representing light field data by capturing a set of images of at least one object in a passive manner at a virtual surface where a center of projection of an acquisition device that captures the set of images lies and generating a representation of the captured set of images using a statistical analysis transformation based on a parameterization that involves the virtual surface.
1 . A method of representing light field data, the method comprising:
capturing a set of images of at least one object in a passive manner at a virtual surface where a center of projection of an acquisition device that captures said set of images lies; and
generating a representation of said captured set of images using a statistical analysis transformation based on a parameterization that involves said virtual surface, wherein said statistical analysis transformation is a training sample principal component analysis.
2 . The method of claim 1 , wherein said virtual surface is a plane.
3 . The method of claim 1 , wherein said parameterization involves a second virtual surface spaced from said virtual surface.
4 . The method of claim 2 wherein said parameterization involves a second virtual surface that is parallel to said virtual surface.
5 . The method of claim 1 , wherein said representation is generated by a single global principal component analysis applied to said set of images captured at said virtual surface.
6 . The method of claim 1 , further comprising:
ordering pixels of each image of said sets of images; and
creating a corresponding set of vectors that are used to generate said representation.
7 . The method of claim 1 , further comprising determining dimensionality of a PCA representation subspace associated with said representation.
8 . The method of claim 7 , wherein said dimensionality is pre-determined.
9 . The method of claim 7 , wherein said determining is based on visual characteristics of said set of images.
10 . The method of claim 1 , wherein said statistical analysis transformation is a direct principal component analysis.
11 . The method of claim 1 , wherein said determining comprises selecting a uniformly distributed sample of said set of images to be used by said training sample principal component analysis.
12 . The method of claim 1 , wherein said determining comprises selecting a nonuniformly distributed sample of said set of images to be used by said training sample principal component analysis.
13 . The method of claim 1 , wherein said determining comprises:
initially selecting J vectors that are used for said training sample principal component analysis;
determining a PCA representation based on said training sample principal component analysis;
generating at most J eigenvectors;
retaining M eigenvectors of said J eigenvectors, wherein M J; and
applying said M eigenvectors to generate said representation.
14 . The method of claim 1 , wherein said representation is generated by a set of local PCA representation subspaces that correspond to a set of local areas of said virtual surface.
15 . The method of claim 14 , further comprising determining dimensionality of each one of said local PCA representation subspaces.
16 . The method of claim 15 , wherein said determining is made subject to a constraint imposed on a total dimensionality of said virtual surface.
17 . The method of claim 14 , wherein said set of local PCA representation subspaces are direct PCA representation subspaces.
18 . The method of claim 14 , wherein said set of local PCA representation subspaces are training sample PCA representation subspaces.
19 . The method of claim 14 , wherein said local areas each have the same area.
20 . The method of claim 14 , wherein said local areas are selected based on geometry of an imaging device at said virtual plane.
21 . The method of claim 14 , wherein said local areas are selected based on a linear discriminating analysis applied to images associated with said virtual surface.
22 . The method of claim 14 , wherein said set of local PCA representation subspaces have variable dimensionality.
23 . The method of claim 22 , wherein said variable dimensionality is selected based on rate-distortion measures.
24 . The method of claim 1 , wherein said representation is generated by a set of local ICA representation subspaces that correspond to a set of local areas of said virtual surface.
25 . The method of claim 1 , further comprising coding eigenvector data associated with images in said virtual surface.
26 . The method of claim 25 , wherein said coding comprises using inverse lexicographic ordering of said eigenvector data to generate corresponding eigenimages.
27 . The method of claim 26 , further comprising adjusting coding of said eigenimages based on rankings of said eigenimages.
28 . The method of claim 27 , wherein said adjusting comprises using a predetermined adjustment.
29 . The method of claim 27 , wherein said adjusting comprises using an eigenvalue magnitude-driven analytic function.
30 . The method of claim 1 , further comprising coding PCA or ICA transformed image vectors associated with each image of said set of images in said virtual surface.