Flexible Addressing 3D LUT for Gamut Mapping
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Solution Overview
Problem
Conventional 3D look-up tables (LUTs) for gamut mapping between different display devices are limited by their fixed sampling granularity and require large memory spaces, making them inefficient for handling the non-linear perception of colors by the human eye.
Innovation Solution
Implementing flexible addressing in 3D LUTs by varying the number of samples along each dimension based on the most significant bits (MSBs) and a non-zero integer (p), allowing for finer granularity and reducing memory requirements through tetrahedral interpolation and scaling factors that account for human perception non-linearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fixed sampling granularity is used in 3D LUT, then the implementation is simple, but the memory space required is large and the interpolation quality is insufficient
Solution Approach 1:
The patent divides the color space into multiple regions with different sampling densities. High-density regions are applied to areas where human perception is more sensitive to color differences, while low-density regions are applied to areas where perception is less sensitive. This segmentation allows the system to achieve high interpolation quality in critical areas while reducing overall memory requirements.
Solution Approach 2:
The patent implements variable sampling granularity where different regions of the color space have different numbers of samples along each dimension. Specifically, the number of samples is adjusted based on the local perception sensitivity and the position in color space, allowing fine granularity where needed and coarse granularity where acceptable, thus optimizing the balance between interpolation quality and memory usage.
2Measurement precision
If the number of samples along each dimension is increased to improve interpolation quality, then the color mapping accuracy improves, but the memory space required increases significantly
Solution Approach 1:
The patent introduces dynamic sampling granularity where the number of samples along each dimension is not fixed but varies based on the input color values. The system uses scaling factors and offset values to dynamically adjust the effective resolution in different regions of the color space, allowing high accuracy when needed while maintaining lower memory requirements overall.
Solution Approach 2:
The patent changes the sampling parameters (number of samples along each dimension) based on the position in color space and perception sensitivity. By using different sampling densities in different regions and applying scaling transformations, the system achieves high color mapping accuracy in critical areas without requiring uniformly high sampling across the entire color space, thus reducing total memory requirements.
3Adaptability or versatility
If uniform sampling is used across the entire color space, then the implementation is straightforward, but it does not account for human perception non-linearity
Solution Approach 1:
The patent introduces scaling factors and offset values as intermediary parameters that transform the input color values before addressing the LUT. These intermediaries allow the system to account for human perception non-linearity by adjusting the effective sampling density in different regions, bridging the gap between uniform memory structure and non-uniform perception requirements.
4Quantity of substance
If flexible addressing with variable sampling granularity is implemented, then memory space is reduced and perception accuracy is improved, but the addressing complexity increases
Solution Approach 1:
The patent performs preliminary calculations of scaling factors and offset values based on the input color components before generating the final LUT address. By pre-computing these transformation parameters and organizing the sampling grid in advance, the system reduces the complexity of the addressing logic during actual color mapping operations, making the flexible addressing more manageable.
Data Source
AI summary
A three-dimensional (3-D) look up table (LUT) can be accessed using an address decoder to identify a plurality of vertices in the 3-D LUT based on a number (m) of most significant bits (MSBs) of three coordinate values representative of a first color and a non-zero integer (p). The three coordinate values are determined by a source gamut. One or more memories store component values representative of a plurality of second colors determined by a destination gamut. The component values are stored at memory locations associated with the plurality of vertices. An interpolator maps the input color to an output color in the destination gamut based on the component values.