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;
ordering pixels of each image of said sets of images;
creating a corresponding set of vectors that are used to generate said representation; 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 an iterative 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 , 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.
6 . The method of claim 1 , further comprising determining dimensionality of a PCA representation subspace associated with said representation.
7 . The method of claim 6 , wherein said dimensionality is pre-determined.
8 . The method of claim 6 , wherein said determining is based on visual characteristics of said set of images.
9 . The method of claim 1 , wherein said statistical analysis transformation is a direct principal component analysis.
10 . The method of claim 1 , wherein said determining comprises selecting a uniformly distributed sample of said set of images to be used by said iterative principal component analysis.
11 . The method of claim 1 , wherein said determining comprises selecting a nonuniformly distributed sample of said set of images to be used by said iterative principal component analysis.
12 . The method of claim 1 , wherein said determining comprises:
a) determining an initial PCA representation based on an initial sample set of eigenvectors of said set of images;
b) generating an initial set of M eigenvectors;
c) performing an iteration with all of said M eigenvectors and an original vector from said set of images excluding said sample set and generating a new set of eigenvectors;
d) repeat step c) until all original vectors have been used during said iteration step c) so as to generate a final set of M eigenvectors; and
e) applying said final set of M eigenvectors to generate said representation.
13 . 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.
14 . The method of claim 13 , further comprising determining dimensionality of each one of said local PCA representation subspaces.
15 . The method of claim 14 , wherein said determining is made subject to a constraint imposed on a total dimensionality of said virtual surface.
16 . The method of claim 13 , wherein said set of local PCA representation subspaces are direct PCA representation subspaces.
17 . The method of claim 13 , wherein set of local PCA representation subspaces are iterative PCA representation subspaces.
18 . The method of claim 13 , wherein said local areas each have the same area.
19 . The method of claim 13 , wherein said local areas are selected based on geometry of an imaging device at said virtual plane.
20 . The method of claim 13 , wherein said local areas are selected based on a linear discriminating analysis applied to images associated with said virtual surface.
21 . The method of claim 13 , wherein said set of local PCA representation subspaces have variable dimensionality.
22 . The method of claim 21 , wherein said variable dimensionality is selected based on rate-distortion measures.
23 . 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.
24 . The method of claim 1 , further comprising coding eigenvector data associated with images in said virtual surface.
25 . The method of claim 24 , wherein said coding comprises using inverse lexicographic ordering of said eigenvector data to generate corresponding eigenimages.
26 . The method of claim 25 , further comprising adjusting coding of said eigenimages based on rankings of said eigenimages.
27 . The method of claim 26 , wherein said adjusting comprises using a predetermined adjustment.
28 . The method of claim 26 , wherein said adjusting comprises using an eigenvalue magnitude-driven analytic function.
29 . 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.
30 . The method of claim 24 , further comprising transmitting coded eigenvector data based on said coding.
31 . The method of claim 24 , further comprising decoding eigenvector data based on said coding.