Streaming Image Processing for Low-Buffer Hologram Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing methods for holographic projection require large data storage and buffering capacities due to the need to store and buffer the entire primary image data for kernel sub-sampling, which is inefficient and slows down processing speed.
Innovation Solution
The use of data streaming to synchronize pixel values of the primary image with kernel values, allowing for reduced data storage and buffering by generating a secondary image through kernel sub-sampling, which can be processed in real-time to form a hologram.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire primary image data is stored and buffered for kernel sub-sampling, then complete image data is available for processing, but data storage and buffering capacities are excessively large
Solution Approach 1:
The patent extracts only the necessary pixel data from the primary image for kernel sub-sampling operations. Instead of buffering the entire primary image, the system retrieves and processes only the specific pixel values needed for each kernel operation, significantly reducing the quantity of data that must be stored and buffered while maintaining processing reliability.
Solution Approach 2:
The patent performs preliminary calculations and preparations for kernel sub-sampling before the actual hologram generation. By pre-computing certain parameters and organizing data in advance, the system reduces the need for large buffers during the main processing phase, as critical data transformations are already completed.
2Ease of operation
If large buffering capacity is allocated for primary image data, then processing can be performed, but processing speed is reduced
Solution Approach 1:
The system extracts and processes only the necessary pixel data for kernel operations rather than manipulating the entire primary image buffer. This extraction approach reduces memory access overhead and accelerates processing speed by focusing computational resources on relevant data only.
Solution Approach 2:
The patent segments the primary image data into smaller manageable units corresponding to kernel sampling regions. By dividing the large image buffer into discrete processing blocks, the system can process multiple segments in parallel or sequentially with reduced memory footprint, thereby improving overall processing throughput.
3Manufacturing precision
If the primary image is upscaled to increase resolution, then holographic reconstruction quality improves, but the size of primary image data increases significantly
Solution Approach 1:
The patent applies local quality enhancement by performing kernel sub-sampling operations that selectively process regions of the upscaled image. Instead of uniformly processing all high-resolution pixels, the system applies different processing qualities and levels of detail to different regions, maintaining holographic reconstruction quality where needed while reducing overall data requirements.
Solution Approach 2:
The system performs partial processing of the upscaled image data by applying kernel sub-sampling to generate the secondary image at the desired resolution. Rather than processing the entire high-resolution dataset, the patent uses selective sampling and interpolation techniques that achieve the necessary holographic quality with reduced computational effort and data handling.
4Manufacturing precision
If kernel sub-sampling is performed on the entire primary image, then a complete secondary image is generated, but data storage requirements depend on input data size rather than output data size
Solution Approach 1:
The patent extracts only the essential pixel information needed for secondary image generation from the primary image. By using kernel sub-sampling that selectively samples and interpolates data, the system generates a complete secondary image while storing and buffering only the minimal necessary input data, decoupling storage requirements from the full primary image size.
Solution Approach 2:
The system performs preliminary organization of primary image data into kernel-sized blocks before sub-sampling. This preliminary structuring allows the processing architecture to allocate storage buffers based on kernel operation requirements rather than the full image dimensions, simplifying the data storage architecture to match the actual processing needs.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
An image processing engine and method of forming a hologram of a target image for projection using data streaming. An input or primary image is sub-sampled using a kernel and the secondary image output used to generate a hologram of the target image. A technique of kernel sub-sampling using a plurality of two or more data streams provides improvements in efficiency, including reduced data storage requirements and increased processing speed.