Image Data Processing for Holographic FFT Efficiency
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Solution Overview
Problem
Current image data processing apparatuses for generating holographic images require significant computational resources and time due to the need for complex operations like Fourier transforms, which is particularly challenging for portable devices with limited power and size.
Innovation Solution
The implementation of a method that performs a fast Fourier transform simultaneously on converted first and second image data using a real part operation method and an imaginary part operation method, increasing computational efficiency for processing 3D images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Fourier transform is performed on image data to reproduce holographic images, then image quality is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the image data into first image data and second image data corresponding to different depth information. The processor performs Fourier transforms on these segmented data separately, allowing for more efficient processing while maintaining image quality. This segmentation enables parallel processing and reduces the computational burden on a single data stream.
Solution Approach 2:
The patent applies Fast Fourier Transform (FFT) algorithms which perform partial computations compared to a complete Fourier transform. By using efficient algorithms that compute only the necessary components and leveraging the structure of the depth-separated data, the processing time is reduced while still achieving the required image quality for holographic reproduction.
2Measurement precision
If complex operations like Fourier transform are performed to generate holographic images, then image quality is improved, but computational resources and power consumption increase
Solution Approach 1:
By segmenting image data into depth-based components (first and second image data), the patent enables more efficient computational processing. This segmentation allows the system to process simpler data structures separately rather than handling complex full-depth data in one operation, reducing overall computational resource requirements and power consumption.
Solution Approach 2:
The patent uses Fast Fourier Transform algorithms which are optimized copies or approximations of the complete Fourier transform. These FFT implementations provide sufficient accuracy for holographic image generation while consuming significantly less computational power, making the system feasible for portable devices with limited energy resources.
3Productivity
If depth information is processed separately for first and second image data, then processing efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments image data based on depth information into first and second image data. This segmentation is implemented through software processing algorithms that divide and process data streams, avoiding the need for additional physical hardware components. The segmentation enables parallel processing paths that improve efficiency while maintaining relatively simple device architecture.
Data Source
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AI summary
A method and apparatus for converting first image data corresponding to a first depth and second image data corresponding to a second depth may be used for displaying a 3D image represented by the first image data and the second image data and performing FFT on the converted first image data and the converted second image data.