# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 1 Introduction  
Decolorization is a process of converting a color image to an image deprived of color, i.e., to a grayscale image. Grayscale images are widely used in image processing and computer vision because of the convenience of dealing with data consisting of one information channel. Color images, on the other hand, are generally more common than grayscale images in modern digital media. Color images can be represented in various color spaces, each with distinct characteristics and applications. RGB (Red, Green, Blue) color space, consisting of three color channels, is widely regarded as the most common color space for digital applications due to its compatibility with modern electronic displays and its ability to represent a vast range of colors. The three RGB channels require a large storage capacity and are computationally expensive [1]. Grayscale images are more suitable for transmission and storage [2], as well as for reducing computational requirements [3]. Conversion of a color image to a grayscale image is indispensable for black-and-white printing and displaying, and also for many single-channel image processing applications, e.g., edge detection, image segmentation, color augmentation, and image stylization [4, 5]. It is rarely explicitly defined how exactly grayscale images are obtained from color images in the mentioned computer vision applications; the main erroneous assumption is that the conversion does not have a big impact on the algorithm efficacy [3]. The objectiveof decolorization is to preserve the important information from color channels as much as possible, even though some loss is expected and unavoidable [6]. In recent times, various approaches to decolorization have been divided into early (basic) decolorization methods, local and global decolorization methods, and deep learning-based decolorization methods [7; 8]. Early (basic) image decolorization methods transform color images to grayscale based on simple pixel intensity conversion formulas. Both local and global decolorization methods attempt to maintain visual fidelity to the original image by keeping essential features that affect how humans perceive depth, texture, and content in the grayscale output. Local image decolorization methods pay attention to local contrasts or areas within an image, providing detailed preservation of visual information. Global image decolorization methods aim to maintain consistent grayscale transformations by applying uniform decolorization across the whole image, respecting the overall image structure. Deep learning image decolorization methods use neural networks to analyze complex image features, achieving grayscale conversions with enhanced detail preservation. Evaluating image decolorization methods is challenging because the quality of decolorized images is subjective and context dependent. It is influenced by how accurately the grayscale image represents object boundaries and textures and how certain parts of the image naturally draw the attention of the viewer, guiding the focus from prominent areas to less noticeable details. In [6], an extensive perceptual evaluation was conducted. The results of the experiments, together with the dataset developed in [6] are the benchmark for image decolorization. Root mean square contrast difference (RMS), normalized root mean square contrast difference (NRMS), and gradient recall ratio (GRR) can be used as objective image quality assessment metrics for decolorization results [9] together with the feature similarity index (FSIM) [10]. RMS estimates the difference between the intensity of every pixel and the mean intensity of the image. NRMS calculates the Frobenius norm between the intensity of each channel in the original color image and the intensity of the decolorized image. GRR evaluates the image quality based on the image details; and the gradients along the vertical and horizontal axes. The gradient refers to the frequency of changes in the intensity of a pixel or a group of pixels in the grayscale image and the color image channels.