Parametric Pixel Beam Representation for 4D Light-Field Data
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Solution Overview
Problem
Current methods for representing and processing 4D light-field data are inefficient due to large storage requirements and lack of standardization across different light-field cameras, leading to cumbersome storage and processing challenges.
Innovation Solution
A compact representation format for 4D light-field data is developed, utilizing a ray-based approach where pixel beams are sorted to reduce randomness and implicit parameters are constrained, allowing for a more efficient storage and processing of light-field data by representing it with six to ten parameters, specifically using a hyperboloid of one sheet to describe pixel beams and sorting generating rays to reduce dimensionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 4D light-field data is represented using conventional methods (raw images, sub-aperture images, or epipolar images), then the data can be acquired and stored, but the storage space required becomes extremely large (several TB) and processing becomes cumbersome
Solution Approach 1:
The patent changes the parameter representation from conventional image-based formats to a parametric format using six to ten parameters per pixel beam. Each pixel beam is represented by parameters including two generating rays (each with position and direction) and a distance parameter, fundamentally altering how light-field data is encoded to reduce storage requirements while maintaining processing capability
Solution Approach 2:
The patent transitions from representing light-field data in traditional 2D image planes to a 4D parameter space using hyperboloid geometry. By representing pixel beams as hyperboloids of one sheet with specific geometric parameters, the system achieves more efficient data compression and representation in a higher-dimensional parameter space
2Adaptability or versatility
If different light-field cameras (plenoptic, camera arrays, conventional cameras) are used to acquire 4D light-field data, then diverse acquisition capabilities are achieved, but each camera produces proprietary file formats lacking standardization
Solution Approach 1:
The patent creates a universal parametric representation format that can represent pixel beams from any light-field acquisition system (plenoptic cameras, camera arrays, or conventional cameras). The standardized six-to-ten parameter format serves multiple functions across different camera types, enabling interchangeability and compatibility while preserving the unique acquisition capabilities of each system
Solution Approach 2:
The patent standardizes the data format by transforming diverse camera-specific formats into a unified parametric representation. By changing the representation parameters to a consistent set of six to ten parameters describing hyperboloid geometry, the system achieves format compatibility across different acquisition devices while maintaining their respective acquisition advantages
3Measurement precision
If pixel beams are represented with detailed geometric accuracy using hyperboloid surfaces, then precise light-field representation is achieved, but the computational complexity increases
Solution Approach 1:
The patent reduces computational complexity by changing from full surface geometry representation to a simplified parametric model. By representing pixel beams with six to ten key parameters (two generating rays and distance) rather than complete hyperboloid surface data, the system maintains geometric accuracy for light-field reconstruction while significantly reducing computational burden for storage and processing
Data Source
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AI summary
There are several types of plenoptic devices and camera arrays available on the market, and all these light field acquisition devices have their proprietary file format. However, there is no standard supporting the acquisition and transmission of multidimensional information. It is interesting to obtain information related to a correspondence between pixels of a sensor of said optical acquisition system and an object space of said optical acquisition system. Indeed, knowing which portion of the object space of an optical acquisition system a pixel belonging to the sensor of said optical acquisition system is sensing enables the improvement of signal processing operations. The notion of pixel beam, which represents a volume occupied by a set of rays of light in an object space of an optical system of a camera along with a compact format for storing such information is thus introduce.