Shannon Wavelet Holographic Image Processing
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Solution Overview
Problem
Conventional 3D video techniques cause discomfort due to fixed image display, and existing holographic image compression methods are inefficient and sensitive to viewer position changes, leading to high calculation complexity and memory usage.
Innovation Solution
The method employs Shannon wavelets for decomposing holographic images, utilizing their rectangular Fourier transform to select relevant wavelet coefficients for reconstructing sub-holograms, reducing sensitivity to viewer position and optimizing calculation times and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional 3D video techniques are used to display images on a fixed plane, then the display system is simple, but the audience experiences discomfort and sickness due to inability to focus naturally on moving objects
Solution Approach 1:
The patent changes the fundamental parameter of image representation from conventional 2D video frames to holographic patterns encoded with wavelet coefficients. This allows the display to reconstruct 3D scenes with intermediate planes, enabling natural human eye focusing while maintaining a relatively simple display device architecture.
2Loss of information
If Gabor wavelets are used to decompose holographic images and select coefficients for sub-hologram reconstruction, then data compression is achieved, but the reconstruction becomes extremely sensitive to observer position changes
Solution Approach 1:
The patent changes the wavelet type from Gabor to Shannon wavelets. Shannon wavelets have a rectangular frequency spectrum that provides more uniform energy distribution across frequencies, making the holographic reconstruction less sensitive to observer position changes while maintaining compression effectiveness.
Solution Approach 2:
The patent implements dynamic selection of wavelet coefficients based on observer position. The system adapts which coefficients to transmit by calculating the subset relevant to the current observer position, balancing compression efficiency with robustness to position changes.
3Reliability
If a large number of wavelet coefficients are transmitted to ensure accurate hologram reconstruction from multiple viewpoints, then reconstruction quality is maintained, but the quantity of data to be processed increases significantly
Solution Approach 1:
The patent extracts only the necessary subset of wavelet coefficients required for reconstructing the sub-hologram visible from the observer's position. By identifying and transmitting only these relevant coefficients rather than all coefficients, the system maintains reconstruction quality while significantly reducing data quantity.
Solution Approach 2:
The patent transmits a partial set of coefficients that is sufficient for the specific observer position rather than all possible coefficients for all positions. This partial action approach achieves the minimum necessary data transmission for acceptable reconstruction quality at each viewpoint.
4Measurement precision
If new Gabor wavelets are calculated at every slight movement of the observer, then reconstruction accuracy is maintained, but calculation time and memory requirements increase significantly
Solution Approach 1:
The patent pre-calculates and stores Shannon wavelet coefficients that can be used for a range of observer positions. Instead of calculating new wavelets at every position change, the system selects from pre-computed coefficients, significantly reducing real-time calculation time while maintaining adequate reconstruction accuracy.
Solution Approach 2:
The patent makes the wavelet coefficients universal across multiple observer positions. Shannon wavelets with their rectangular frequency spectrum provide a broader spatial frequency coverage, allowing a single set of coefficients to serve multiple viewing positions rather than requiring position-specific wavelets.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables robust and efficient holographic image reconstruction with reduced calculation time and memory requirements, allowing for real-time processing and minimizing the need for recalculating wavelets with slight viewer movements.
Implementation Method 1
a subset of relevant wavelet coefficients for the reconstruction of a sub-hologram visible for the at least one observer from at least one observation point being selected from a decomposition of at least one holographic image on a wavelet basis
Implementation Method 2
utilizing their rectangular Fourier transform to select relevant wavelet coefficients for reconstructing sub-holograms
Implementation Method 3
the directional character of the spectrum associated with a Gabor wavelet and the direction of the light after diffraction through this wavelet at a point of the holographic image
Data Source
AI summary
A method for processing a sequence of holographic images with a view to playing the images back on a holographic display device to at least one viewer. A subset of wavelet coefficients relevant for the reconstruction of a sub-hologram visible for the at least one viewer from at least one viewing point is selected from a decomposition of at least one holographic image on a wavelet basis and information representative of a location of the at least one viewer in a repository of the display device. In the method, decomposition of the at least one holographic image is carried out on a Shannon wavelet basis.