# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 1 Introduction
FSIM is a metric based on the preference of the human visual system for low-level features in understanding an image [10]. FSIM can be adapted to compare the original color image with the decolorized image by paying attention to gradient similarity, luminance similarity, and contrast similarity. The structural similarity index (SSIM) can also be used for evaluating image decolorization results [10; 11] since it consists of the luminance, contrast, and structure comparisons between images, serving as a good indicator of the perceived image quality because of the adaptation of the human visual system to the extraction of the visual structural information. In [12], the color contrast preserving ratio (CCPR), the color content fidelity ratio (CCFR), and their harmonic mean, the E-score, were introduced. Since CCPR, CCFR, and the E-score are dependent on the threshold of perceivable difference between two colors, the threshold-independent area (TIA) and the weighted threshold-independent area (WTIA) were developed [13]. TIA is the area between the \(x\)-axis and the linear regression estimation of the E-score metrics for various threshold values. WTIA uses weighted E-scores [13]. Selecting the decolorization method depends on the intended application. In medical imaging, such as diagnosing bone fractures, preserving local contrasts and fine structural details is crucial [14; 15; 16]. Methods based on human perception [17; 18; 19; 20] enhance critical details by aligning with the human visual system. FSIM, a metric evaluating both structural and perceptual aspects, is suitable for this purpose. In satellite imaging, decolorization focuses on maintaining global contrast and luminance distribution to preserve significant features like land formations and urban patterns while reducing noise. Methods prioritizing global contrast preservation [21; 22] are ideal. GRR metric ensures that structural details and prominent contrasts, critical for interpreting satellite imagery, are evaluated effectively. The primary motivation behind our research is to address the challenges associated with decolorization which is an important preprocessing step in the subsequent image processing tasks. For example, object segmentation is performed on decolorized images, making decolorization a crucial preparatory step (unless segmentation is specifically color-based). Despite its importance, decolorization is often overlooked, with default methods integrated into programming libraries. However, selecting an appropriate decolorization method can significantly improve subsequent image processing tasks. The main aim of this research is to examine and compare seven different decolorization methods (basic conversion from RGB to YUV format using ITU Recommendations 601 [17], 709 [18], and 2020 [19], basic conversion from RGB to LAB color space, method using the cumulative distribution function (CDF) of color channels, global decolorization method, and decolorization method based on deep learning) to identify the most effective techniques for converting color images into grayscale. The study focuses on understanding how the methods impact the quality of decolorized images and their applicability in subsequent image processing tasks, such as object segmentation and edge detection. This analysis facilitates the selection of appropriate measures for evaluating decolorization while providing insight into the performance of the algorithms, which is crucial for applications in real-world scenarios where efficiency and accuracy are key. In this research, a dataset with diverse images was selected, including a broad range of content, colors, and image origins, to gain insights into the properties of decolorization methods and their applicability across various domains, which this diversity of content in the dataset enables. The rest of this paper is structured as follows: In Section 2, a more detailed examination of the state-of-the-art is provided. Section 3 gives an extensive description of the decolorization methods used for the comparison.