Color Space Conversion Using 1D Embeddings for Lower Compute Load
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Solution Overview
Problem
Conventional color space conversion for images is computationally expensive and memory-intensive due to the large size of image data, making it inefficient for tasks like object recognition and edge detection.
Innovation Solution
A neural network, such as a convolutional variational autoencoder, reduces multi-dimensional color data to a single-dimensional embedding in a latent space, which is then decoded into a different color space, reducing memory and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color space conversion is performed on full-resolution image data, then accurate color transformation is achieved, but computational cost and memory requirements become onerous
Solution Approach 1:
The image data is segmented into superpixels (groups of adjacent pixels) rather than processing individual pixels. This segmentation reduces the total number of processing units from thousands or millions of pixels to hundreds or thousands of superpixels, dramatically reducing computational cost and memory requirements while preserving color conversion accuracy within each superpixel region.
Solution Approach 2:
The patent transforms the problem from pixel-level color space conversion to superpixel-level conversion by introducing a spatial grouping dimension. This dimensional change allows the system to process color data at a coarser granularity, reducing the computational burden while maintaining the essential color information needed for accurate transformation between color spaces.
2Measurement precision
If color data is processed at full pixel resolution, then processing accuracy is maintained, but memory requirements become excessively large
Solution Approach 1:
By segmenting the image into superpixels, the patent reduces the quantity of color data that must be stored in memory during processing. Instead of holding color values for every individual pixel, the system stores and processes color data for superpixel representatives, significantly reducing memory consumption while preserving the visual and color information necessary for accurate color space conversion.
3Reliability
If conventional color conversion methods are used, then complete color transformation is achieved, but processing time becomes excessive for real-time applications
Solution Approach 1:
The segmentation approach enables parallel processing of superpixels, which can be computed independently and simultaneously. This parallelizability dramatically reduces processing time for color space conversion, making the system suitable for real-time applications such as video processing, while still achieving complete and accurate color transformation across the entire image through systematic superpixel processing.
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.