CoSi

Summary

CoSi technology proposes an innovative approach to efficiently apply the CIEDE2000 color difference formula by deriving mathematical models that are computationally less expensive and discriminative when comparing colors. This technology enables faster and more efficient color similarity calculations, which can lead to significant time and cost savings for companies that need to perform multiple color similarity calculations.

This invention can be used in digital image processing and object detection in the context of computer vision applications. CoSi offers a fast color similarly assessment, which is particularly valuable for video processing.

Problem 

The high computational cost of the CIEDE2000 color difference formula is often too complex to be used in many application scenarios, particularly when computational resources are limited. Color similarity calculations are often required in various industries, such as fashion, graphic design, and manufacturing, to determine the degree of similarity between two colors. However, the use of the CIEDE2000 color difference formula can be computationally intensive, making it challenging to perform color similarity calculations in scenarios where resources are limited.

The problem this technology aims to solve is the high computational cost of the CIEDE2000 color difference formula, which is often too complex to be used in many application scenarios, particularly when computational resources are limited.

Comparable solutions offer simpler models that sacrifice accuracy or completeness, resulting in less reliable results, leading to errors in color similarity determination and identification, and presenting challenges related to image noise and illumination.

Technology

CoSi technology performs faster and more efficient calculations for color similarity, resulting in improved productivity and better-supported decision-making. CoSi's mathematical models are accurate and complete, filtering out color pairs that do not require a full CIEDE2000 color difference calculation, resulting in further time and cost savings.

INESC TEC developed an improved method to calculate CIEDE2000 needing just 5% of the computational power usually required by the conventional implementation of this standard. The solution proposed in this document is based on deriving mathematical models for properties related to the CIEDE2000 color difference formula that are discriminative when comparing colors and computationally less expensive than the formula. This allows for real-time video color analysis and portable use, which is particularly useful when computational resources are limited.

In addition, models were validated using a large dataset and proposed a parallel processing strategy to further improve performance.

Benefits and Advantages

CoSi technology can be applied in any scenario where color similarity calculations are required, with particular value in scenarios where computational resources are limited or where multiple color similarity calculations need to be performed. Possible applications include video processing, dentistry, mineral extraction, and image processing, among others.

The technology may benefit users and companies by enabling faster and more efficient color similarity calculations, which can lead to improved productivity, cost savings, and better decision-making. The proposed approach can also be used to filter out color pairs that are unlikely to have a significant color difference, allowing for more efficient validation of whether it is necessary to compute the full CIEDE2000 color difference or not.

• Faster determination of color similarity without computing the full CIEDE2000 metric: which is often too complex and computationally expensive to be used in many application scenarios. This can lead to significant time and cost savings for companies that need to perform multiple color similarity calculations;
• Derives mathematical models for properties related to the CIEDE2000 formula: which is discriminative when comparing colors and computationally less expensive than the formula. These models can be used to filter out color pairs that are unlikely to have a significant color difference, allowing for more efficient validation of whether it is necessary to compute the full CIEDE2000 color difference or not;
• Validated models using a large dataset: proposing a parallel processing strategy to further improve performance. This can lead to even faster color similarity calculations and better utilization of available computational resources.

Possible Applications and Use Cases

This technology can be applied in any scenario where color similarity calculations are required, particularly in scenarios where computational resources are limited or where multiple color similarity calculations need to be performed. For example:

• Video processing: With the growing demand for video content, the ability to process videos quickly and efficiently is crucial. This technology can enable faster and more efficient color similarity calculations, which can lead to improved video processing and faster turnaround times;
• Dentistry: In the field of dentistry, the color of dental restorations must match the color of natural teeth. This technology can help dentists to determine the color similarity between natural teeth and dental restorations in a more efficient manner, reducing the amount of time and computational resources required for such calculations;
• Mineral extraction: The mining industry can also benefit from this technology, as it can be used to determine the color similarity between minerals and rocks. This can lead to improved efficiency in mineral extraction and reduced costs;
• Image processing: In image processing, color similarity calculations are essential for tasks such as object detection and segmentation. This technology can enable faster and more efficient color similarity calculations, which can improve the accuracy and speed of image processing.

And more! This technology has the potential to bring significant benefits to various industries by enabling faster and more efficient color similarity calculations, improving productivity, reducing costs, and facilitating better decision-making.