Color Profile Creation for Image Accuracy
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Solution Overview
Problem
Social networking systems face challenges in accurately representing colors of images across different devices due to variations in color spaces, leading to inconsistent image rendering and high data requirements for color profile transformations.
Innovation Solution
The system employs a binary search algorithm and simulated annealing algorithm to create a color profile for transforming images from one color space to another, using error metrics like Delta-E and weight calculations based on color frequency, to minimize color transformation errors and optimize polyline segments for efficient data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color space transformation methods are used, then color representation can be maintained, but data requirements and processing bandwidth increase significantly
Solution Approach 1:
The patent extracts only the essential color transformation information needed for accurate representation, storing color profiles selectively based on device characteristics and image content analysis. This reduces the quantity of color data that needs to be processed and transmitted while maintaining accuracy where it matters most.
Solution Approach 2:
The patent applies different color transformation strategies to different regions and elements of the image based on their importance and characteristics. Critical color regions receive full transformation accuracy while less critical areas use simplified transformations, reducing overall data requirements while maintaining perceptual quality.
2Measurement precision
If comprehensive color profiles are used for all devices, then color accuracy is maintained, but processing complexity and bandwidth requirements increase
Solution Approach 1:
The patent segments the color transformation process into multiple stages: device characterization, image analysis, selective profile application, and transformation execution. This segmentation allows each stage to handle only the necessary computations, reducing overall processing complexity while maintaining color accuracy through coordinated multi-stage processing.
Solution Approach 2:
The patent dynamically adjusts color profile parameters based on device capabilities, image content, and viewing conditions. By changing parameters selectively rather than applying fixed comprehensive profiles, the system maintains color accuracy for critical parameters while simplifying others, reducing processing complexity.
3Measurement precision
If device-specific color transformations are applied, then accurate color representation is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary device characterization and color profile generation in advance, storing the results for rapid retrieval during image transformation. This preliminary action eliminates the need for complex real-time computations, significantly reducing processing time while maintaining accuracy through pre-computed transformation parameters.
Solution Approach 2:
The patent uses simplified color transformation models that replicate the essential behavior of complex device-specific transformations. By creating and using these simplified copies for routine transformations, the system achieves acceptable color accuracy with dramatically reduced computational requirements and processing time.
Data Source
AI summary
Techniques for accurate color representation of images stored within a social networking system. In an embodiment, an error metric and a target error threshold are determined. A binary search algorithm and a simulated annealing algorithm are performed. A color profile for transforming an image in a first color space to a second color space is created based on the binary search algorithm, the simulated annealing algorithm, the error metric, and the target error threshold. Determination of the error metric may comprise determining a frequency with which a color occurs in the image, assigning a weight to the color based on the frequency, and calculating the error metric based on the weight. Determination of the error metric may comprise dividing the image into a plurality of sections, assigning a plurality of importance values to the plurality of sections, and calculating the error metric based on the plurality of importance values.