Neural Color Space Conversion With Single-Dimensional Embeddings
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Solution Overview
Problem
Conventional color space conversions for images are computationally expensive and memory-intensive due to the large size of image data, making it challenging to efficiently convert between different color spaces for tasks like object recognition and edge detection.
Innovation Solution
A neural network, such as a convolutional variational autoencoder, reduces the dimensionality of color data from a first color space to a single-dimensional embedding, which is then decoded into a second color space, enabling efficient conversion with reduced memory and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color space conversion methods are used on full-resolution image data, then accurate color conversion is achieved, but computational cost and memory requirements become onerous
Solution Approach 1:
The patent segments the image processing task by dividing it into multiple resolutions. The neural network processes the image at a reduced resolution first, then progressively refines the results at higher resolutions. This segmentation allows accurate color conversion to be achieved at multiple scales while maintaining computational efficiency by avoiding direct processing of full-resolution data throughout the entire conversion pipeline.
Solution Approach 2:
The patent introduces a resolution dimension by processing the image at multiple different resolutions rather than a single full resolution. The neural network takes multi-resolution input and produces multi-resolution output, allowing the system to benefit from both low-resolution computational efficiency and high-resolution accuracy by combining results across the resolution dimension.
2Measurement precision
If conventional color space conversion methods are used on full-resolution image data, then accurate color conversion is achieved, but memory requirements become onerous
Solution Approach 1:
The patent segments the image data into multiple resolution levels, processing each level separately through the neural network. By working with smaller downsampled versions of the image during intermediate processing stages, the memory footprint is significantly reduced while still preserving the ability to produce accurate full-resolution output through progressive refinement.
Solution Approach 2:
The patent adds a resolution dimension to the processing pipeline, allowing the system to operate efficiently at lower memory requirements by processing multiple scaled versions of the image. This dimensional approach enables accurate color conversion without requiring all full-resolution data to be held in memory simultaneously throughout the conversion process.
3Ease of manufacture
If color data is represented in RGB color space, then standard encoding is used, but edge detection and object recognition are less effective compared to LAB color space
Solution Approach 1:
The patent changes the color space parameters by converting from RGB to LAB color space within the neural network processing pipeline. This parameter transformation allows the system to maintain the simplicity of standard RGB encoding for input while leveraging the superior edge detection and object recognition properties of LAB color space during processing, with results that can be converted back to RGB if needed.
Data Source
AI summary
Exemplary embodiments may provide an approach to converting multidimensional color data for an image encoded in a first color space into an intermediate form that is a single dimensional value. The exemplary embodiments may then decode the intermediate form value to produce an encoding of the color data that is encoded in a second color space that differs from the first color space. In this manner, the data for the image may be efficiently converted from an encoding in the first color space into an encoding in the second color space.