Representation and coding of light field data

Light Fields and Lumigraphs represent 4D parameterizations of the plenoptic function. Given the large amount of data and the nature of such representations, there are two main requirements for the effective processing of light fields. The light field data must be compressed efficiently for storage or communication purposes. Also, the coded light field representation should provide random access to the data for rendering purposes. Various techniques have been proposed to enable a more efficient representation and coding of the data, such as using vector quantization and Lempel-Ziv entropy coding of data, JPEG coding, or extensions of predictive coding schemes. Predictive coding provides very good compression efficiency but also introduces referencing-related dependencies in the coded data that hinder the data access required for view synthesis. In this paper, we present a new approach for representing and coding Light Field data by using a statistical representation based on Principal Components Analysis. The proposed approach offers an efficient representation and coding in the rate-distortion sense, enables random access to pixels in the images containing information required for virtual view synthesis, and provides straightforward scalability.

[1]  Bernd Girod,et al.  Adaptive block-based light field coding , 1999 .

[2]  Wei-Chao Chen,et al.  Light field mapping: efficient representation and hardware rendering of surface light fields , 2002, SIGGRAPH.

[4]  Donald S. Fussell,et al.  Uniformly Sampled Light Fields , 1998, Rendering Techniques.

[5]  Dinesh Manocha,et al.  Spatially-encoded far-field representations for interactive walkthroughs , 2001, MULTIMEDIA '01.

[6]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[7]  Harry Shum,et al.  Plenoptic sampling , 2000, SIGGRAPH.

[8]  Richard Szeliski,et al.  The lumigraph , 1996, SIGGRAPH.

[9]  Jin Li,et al.  Compression of Lumigraph with multiple reference frame (MRF) prediction and just-in-time rendering , 2000, Proceedings DCC 2000. Data Compression Conference.

[10]  Takeshi Naemura,et al.  Ray-based approach to integrated 3D visual communication , 2001, Optics East.

[11]  Marcus A. Magnor,et al.  Two approaches to incorporate approximate geometry into multi-view image coding , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[12]  Insung Ihm,et al.  Rendering of spherical light fields , 1997, Proceedings The Fifth Pacific Conference on Computer Graphics and Applications.

[13]  B. V. K. Vijaya Kumar,et al.  Efficient Calculation of Primary Images from a Set of Images , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Paul Lalonde,et al.  Interactive Rendering of Wavelet Projected Light Fields , 1999, Graphics Interface.

[15]  M. Landy,et al.  The Plenoptic Function and the Elements of Early Vision , 1991 .

[16]  Leonard McMillan,et al.  Plenoptic Modeling: An Image-Based Rendering System , 2023 .

[17]  Harry Shum,et al.  Rendering with concentric mosaics , 1999, SIGGRAPH.

[18]  Markus Flierl,et al.  Multi-hypothesis prediction for disparity compensated light field compression , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).