Latent Color Space Transformation via Variational Autoencoder
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Solution Overview
Problem
Existing methods for generating and adjusting three-dimensional look-up tables (3D LUTs) for color transformation are labor-intensive, computationally costly, and struggle with real-time adaptation to image content, often resulting in low accuracy due to the high number of control points and restrictive assumptions about linear color changes.
Innovation Solution
The use of a variational autoencoder to transform and modify latent color space mappings, allowing for efficient compression and decoding of color transformations into a low-dimensional latent variable space, which can be easily manipulated to create new color mappings for both traditional and AI-based algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a high number of sampling points (e.g., 17*17*17*3) is used in 3D LUT to ensure color transformation accuracy, then manufacturing precision is improved, but device complexity and labor cost increase significantly
Solution Approach 1:
The patent segments the high-dimensional 3D LUT adjustment problem into multiple independent low-dimensional color adjustment functions (brightness, saturation, contrast, hue, white balance). Each function operates on a simplified subset of the color space, reducing the complexity from 17*17*17*3 control points to manageable independent adjustments while maintaining overall color transformation accuracy.
Solution Approach 2:
The patent introduces a latent variable space that maps the high-dimensional 3D LUT control parameters into a lower-dimensional representation. By learning a mapping from latent variables to 3D LUT control points, the system reduces the number of parameters that need to be adjusted directly, transforming a complex high-dimensional adjustment problem into a simpler low-dimensional latent space manipulation.
2Device complexity
If a fixed 3D LUT is generated by discrete sampling of color space, then device complexity is reduced, but adaptability to different image contents deteriorates
Solution Approach 1:
The patent transforms the static fixed 3D LUT into a dynamic adaptive system. The 3D LUT is no longer a fixed lookup table but is generated on-demand based on image content analysis. The system dynamically adjusts the color transformation parameters by analyzing image characteristics (color distribution, brightness, saturation) and generating appropriate 3D LUT parameters adaptively for each image.
Solution Approach 2:
The patent implements a feedback mechanism where the system analyzes the actual image content (color histograms, brightness distribution, saturation levels) and uses this information to adjust the 3D LUT parameters. The color adjustment process incorporates feedback from image analysis results, allowing the system to adapt the color transformation to match the specific characteristics of each image rather than applying a fixed transformation.
3Use of energy by moving object
If control points in 3D LUT are adjusted offline to simulate color space transformation, then computing cost during operation is reduced, but productivity and real-time adaptation capability deteriorate
Solution Approach 1:
The patent extracts and separates the computationally intensive 3D LUT generation and adjustment processes from the real-time color transformation operation. The system pre-computes and stores base 3D LUTs for common color adjustment scenarios, then combines these pre-computed LUTs through simple linear operations during real-time processing. This extraction of heavy computation to offline preparation reduces online computing costs while maintaining real-time performance.
Solution Approach 2:
The patent performs preliminary computation by pre-generating and storing multiple base 3D LUTs that represent common color adjustment operations (brightness adjustment LUT, saturation adjustment LUT, contrast adjustment LUT, etc.). These base LUTs are computed in advance and stored for rapid retrieval and combination during real-time color adjustment, eliminating the need for complex real-time 3D LUT generation.
4Device complexity
If linear decomposition of target 3D LUT is assumed for AI color enhancement, then device complexity is reduced, but manufacturing precision deteriorates due to inability to represent nonlinear color changes
Solution Approach 1:
The patent combines multiple base 3D LUTs (each representing a specific color adjustment function) with learned combination weights to create a composite color transformation model. Instead of relying on simple linear decomposition, the system uses a composite structure where multiple specialized LUTs (brightness, saturation, contrast, hue, white balance) are combined with non-linear weighting based on image content analysis, enabling accurate representation of complex non-linear color transformations.
Solution Approach 2:
The patent transforms the color adjustment approach from fixed linear decomposition parameters to dynamically changeable parameters based on image content. The system analyzes image characteristics (color distribution, brightness levels, saturation) and adjusts the combination weights and parameters of base LUTs accordingly. This parameter adaptation allows the model to accurately represent non-linear color changes by changing parameters based on the specific image being processed.
Data Source
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AI summary
A color transformer (900) for transforming a first color space mapping to a second color space mapping, the color transformer comprising one or more processors configured to perform the following steps: (i) transforming (701) the first color space mapping to a latent color space mapping by means of a variational autoencoder; (ii) modifying (702) the latent color space mapping to form a modified latent color space mapping; and (iii) transforming (703) the modified latent color space mapping to the second color space mapping. This can allow for a simpler and more effective color transformation method which can be provided for both traditional and artificial intelligence-based algorithms.