# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 2 Previous Work  
Early decolorization methods most often disregard the chrominance channels [8], i.e., they take the luminance channel of luminance-chrominance color spaces which split brightness and color components to simplify processes like compression and color manipulation. Also, directly processing arbitrarily chosen or maximum-valued red, green, or blue channels of RGB color space is possible. Taking a linear combination of the three channels, e.g., an average (most frequently a weighted average) can also be applied. Early colorization methods are considered basic because they rely on simple algorithms which do not analyze or interpret the content of an image in detail (they do not account for context, texture, or object boundaries within an image). Limited color transfer rules are used, which lack adaptability and often produce unrealistic or imprecise results. More precisely, contrast preservation is not guaranteed, especially in isoluminant regions with different colors. Early (basic) colorization methods are seen as foundational but not sophisticated, especially compared to modern data-driven, context-aware techniques. Local decolorization methods focus on local features, such as edges, and processing neighboring pixels or small image patches to improve decolorization. In [14], distinct adjacent colors are locally preserved by adding high-frequency chrominance information, obtained by a high-pass filter, into the luminance channel. In [15], color and luminance contrasts are considered a gradient field the inconsistency of which is solved by choosing the nearest gradient field in the linear subspace of consistent gradient fields. The method in which grayscale values are incrementally influenced by the colors and intensities of already processed neighboring pixels using a modified floodfill algorithm with randomization is presented in [23]. With local image decolorization methods, the visual quality of grayscale images can be accomplished by preserving fine details and local textures. However, these methods can also introduce artifacts and inconsistencies, such as halo effects or spurious distortions, particularly in uniform color regions or images with isoluminant colors, which may lead to a loss of clarity and detail [4,8]. Global decolorization methods consider the color distribution of the entire image. In [21], a linear decolorization method is introduced; grayscale is expressed as a continuous mapping of the RGB primaries and their saturation; image sampling, dimensionality reduction, and specific methods for color representation and dynamic range scaling are used. In [22], a fast and robust decolorization algorithm using a nonlinear global mapping function of lightness, chroma, and hue is presented. New promising research directions include computing the weights for each pixel in each color channel using the cumulative distribution function (CDF) of each color channel [9] and combining them to obtain the final grayscale result. The human perception of color temperature is used in [24] to achieve fast and effective grayscale conversions. Although global image decolorization methods apply a uniform mapping function to all pixels allowing the efficient processing of high-resolution images, they may fail to preserve the important local contrasts and details. New research approaches take into account both local and global features, aligning with how human vision combines local and global information for perception. In [25], the contrast is calculated with local and nonlocal pixel pairs and the distance between the grayscale contrast and the contrast features in each color channel is measured by the normalized L1 norm. In [26], the combination of enhancing the global image contrast by histogram equalization and enhancing the local contrast by maximizing the local variance is conducted. In [27], image entropy is maximized to preserve global contrast while local edge details are enhanced using the Canny edge retention ratio.