Color Conversion Preserving Image Characteristics via Perceptual Distance
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Solution Overview
Problem
Existing color conversion processes for digital images often result in loss of image characteristics such as color saturation, contrast, and texture due to the use of standard rendering intents, which do not accurately match the gamut of source and destination devices, leading to unsatisfactory reproduction.
Innovation Solution
The method involves determining the perceptual distance between source and destination pixel sets in a perceptually linear color space like S-CIELAB, automatically selecting the rendering intent with the smallest Euclidean distance, and applying it to convert digital images, allowing for partitioning of images to use different rendering intents for optimal preservation of global and local characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard rendering intents are used for color conversion, then the conversion process is simple and fast, but image characteristics such as color saturation, contrast, and texture are lost
Solution Approach 1:
The image is divided into multiple partitions or regions, and different rendering intents are applied to different partitions based on their local characteristics. This segmentation allows the system to preserve image characteristics in critical regions while maintaining efficiency, resolving the contradiction between conversion speed and information preservation.
Solution Approach 2:
The rendering intent is dynamically selected based on local image characteristics rather than applying a single static rendering intent to the entire image. The system analyzes local features such as color saturation, contrast, and texture requirements to adaptively choose the most appropriate rendering intent for each region, thereby preserving image characteristics while maintaining reasonable processing efficiency.
2Device complexity
If a single rendering intent is applied to the entire image, then the conversion process is simple, but both global and local image characteristics cannot be optimized simultaneously
Solution Approach 1:
Different rendering intents are applied to different local regions of the image based on their specific characteristics. For example, regions with high color saturation may use one rendering intent while regions with fine texture details use another. This local quality approach allows optimization of color reproduction accuracy for each region without requiring complex global processing.
Solution Approach 2:
The system dynamically determines the appropriate rendering intent for each local region based on image analysis, rather than using a fixed single rendering intent for the entire image. This dynamic adaptation enables the system to achieve high color reproduction accuracy across diverse image regions while keeping the overall process manageable.
3Loss of information
If photographers and editors manually judge and select rendering intents, then image characteristics can be preserved, but the process is time-consuming and subjective
Solution Approach 1:
The system performs automatic analysis of image characteristics and self-selects the appropriate rendering intent for each region without requiring manual user intervention. The algorithm evaluates local image features such as color distribution, contrast, and texture to autonomously determine the optimal rendering intent, thereby preserving image characteristics while eliminating time-consuming manual selection and subjective variability.
Solution Approach 2:
The system uses feedback from image analysis to automatically adjust and select rendering intents. By analyzing the actual image characteristics and comparing them against the requirements for different rendering intents, the system provides feedback-driven automatic selection that preserves image characteristics without requiring manual user judgment or time-consuming iterative selection.
Data Source
AI summary
A method, system, and computer-readable storage medium are disclosed for color conversion of a digital image. The digital image comprises a source set of pixels. A perceptual distance may be determined between the source set of pixels and a respective destination set of pixels for each of a plurality of rendering intents. A rendering intent corresponding to the smallest perceptual distance may be selected automatically. The source set of pixels may be converted to an output set of pixels using the selected rendering intent.