# Attention-Based Color Difference Perception for Photographic Images
## 1 Introduction  
Color is a key factor in product design, manufacturing, and quality control. In industries such as textiles, printing, coatings, and electronic displays, accurate control and evaluation of CDs are crucial for ensuring product quality and consumer satisfaction [1]. The objective of this paper is to propose a high-precision method for image CD measurement that simulates the perceptual process of the human visual system. The color difference measurement method can be applied in medical imaging, video analysis, image transmission, quality control, automated inspection, and image analysis, and it is of greatsignificance for improving photographic quality, optimizing image processing, and optimizing image display. However, due to the complexity of color perception, developing a formula that can accurately predict and quantify CD has been a long-standing challenge. Modern smartphones employ display technologies like LCD, OLED, and AMOLED, each with its own unique color rendition characteristics [2]. With the continuous advancement of display technology, the color gamut and dynamic contrast ratio that screens can display have significantly improved, presenting new challenges and opportunities for CD measurement. The history of CD measurement dates back to the early 20 th century when scientists began to investigate quantitative methods for measuring color. Early work focused primarily on the physical measurement of color, such as using spectrophotometers to measure the reflective or transmittance properties of colors [3]. With the introduction of the CIE XYZ color space by the International Commission on Illumination (CIE) in 1931, a foundation was laid for the scientific measurement of color. Since then, researchers have proposed various color spaces, and in 1976, the CIE recommended two CD formulas, CIELAB [4] and CIELUV [4], both of which are color spaces based on CIE color-matching functions, providing methods for quantifying CD, mainly used to assess small to medium-sized CDs. CIELAB is widely used due to its simplicity and versatility, while CIELUV is more suitable for specific applications that require a more uniform color space. However, both CIELAB and CIELUV have limitations, particularly when dealing with specific colors or in certain color regions where errors are larger. The CMC CD formula was proposed in 1984 [5], which uses different weighting factors to adjust differences in brightness, chroma, and hue to meet different application needs. With the development of digital image processing and display technology, CD measurement has begun to be applied to digital environments. The development of the CIEDE2000 [6] CD formula took into account the complexity of visual perception. [PERSON] proposed Spatial-CIELAB (S-CIELAB) [7] by simulating the spatial blur of the human visual system to extend CIELAB color measurement and used it to assess the color reproduction errors of digital images. In 2001, [PERSON] introduced a perceptual CD metric based on the Mahalanobis distance, utilizing covariance matrices to account for differences in color attributes [8]. In 2003, [PERSON] proposed expanding the grayscale image quality index into a new color space that is perceptually irrelevant, creating a novel metric for color image fidelity that was evaluated through observer experiments and found to be highly correlated with human perception [9]. In 2005, [PERSON] proposed a measurement model to predict the performance of human judgments in similarity metrics of different images, exploring the model's performance across a broad range of color spaces and identifying the optimal quantification for the selected color space [10]. In 2006, [PERSON] proposed a new algorithm for CD measurement that took into account the importance of different regions in the image and the magnitude of CDs [11].