Compression of two-dimensional data
IEEE Transactions on Information Theory
Concrete mathematics: a foundation for computer science
Concrete mathematics: a foundation for computer science
Entropy and information theory
Entropy and information theory
Elements of information theory
Elements of information theory
Digital video processing
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Rendering with concentric mosaics
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Video Processing and Communications
Video Processing and Communications
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
When is Bit Allocation for Predictive Video Coding Easy?
DCC '05 Proceedings of the Data Compression Conference
Multidimensional Signal, Image, and Video Processing and Coding
Multidimensional Signal, Image, and Video Processing and Coding
A Stochastic Model for Video and its Information Rates
DCC '07 Proceedings of the 2007 Data Compression Conference
IEEE Transactions on Image Processing
ARGOS: automatically extracting repeating objects from multimedia streams
IEEE Transactions on Multimedia
Scanning and Sequential Decision Making for Multidimensional Data–Part I: The Noiseless Case
IEEE Transactions on Information Theory
Statistical models of video structure for content analysis and characterization
IEEE Transactions on Image Processing
Long-term memory motion-compensated prediction
IEEE Transactions on Circuits and Systems for Video Technology
Spectral analysis for sampling image-based rendering data
IEEE Transactions on Circuits and Systems for Video Technology
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The plenoptic function describes the visual information available to an observer at any point in space and time. Samples of the plenoptic function (POF) are seen in video and in general visual content (images, mosaics, panoramic scenes, etc.), and represent large amounts of information. In this paper, we propose a stochastic model to study the compression limits of a simplified version of the plenoptic function. In the proposed framework, we isolate the two fundamental sources of information in the POF: the one representing the camera motion and the other representing the information complexity of the "reality" being acquired and transmitted. The sources of information are combined, generating a stochastic process that we study in detail. We first propose a model for ensembles of realities that do not change over time. The proposed model is simple in that it enables us to derive precise coding bounds in the information-theoretic sense that are sharp in a number of cases of practical interest. For this simple case of static realities and camera motion, our results indicate that coding practice is in accordance with optimal coding from an information-theoretic standpoint. The model is further extended to account for visual realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Examples with synthetic sources suggest that within our proposed model, common hybrid coding using motion/displacement estimation with DPCM performs considerably suboptimally relative to the true rate-distortion bound.