###### Abstract  
The visualization of 2D clinical data often relies on color-coded images, but different colormaps can introduce cognitive biases, impacting result interpretation. Moreover, when using color for diagnosis with multiple biomarkers, the application of distinct colormaps for each parameter can hinder comparisons. Our aim was to introduce a visualization technique that utilizes the hue (H), saturation (S), and value (V) in a single image to convey multi-parametric data on various optical properties in an effective manner. To achieve this, we conducted a study involving two datasets, one comprising multi-modality measurements of the human aorta and the other featuring multiple parameters of dystrophic mice muscles. Through this analysis, we determined that H is best suited to emphasize differences related to pathology, while V highlights high-spatial-resolution data disparities, and color alterations effectively indicate changes in chemical component concentrations. Furthermore, encoding structural information as S and V within the same image assists in pinpointing the specific locations of these variations. In cases where all data are of a high resolution, H remains the optimal indicator of pathology, ensuring results' interpretability. This approach simplifies the selection of an appropriate colormap and enhances the ability to grasp a sample's characteristics at a single glance. H +
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chroma variations is worse than that of luminance [8]. These claims lead to the assertion that a rapidly changing (high spatial frequency/high resolution) dataset would benefit from the use of colormaps with perceptually uniform luminance, while a lower-resolution dataset could be better represented from a chroma-variation-first colormap to exploit the dark- or hotspot-is-more biases. Given the intricate relationship between perception biases and data visualization, numerous studies have explored the impact of different colormaps on the visualization and representation of identical 2D data [9], the creation of task-customized colormaps [10; 11; 12; 13], as well as the best way to build a color-based representation [14; 15; 16; 17]. Conventional colormaps, featuring diverse spectral arrangements, have the potential to elicit varied perceptual responses due to the interplay of color and luminance gradients. Researchers have examined how these colormap properties influence critical factors such as data clarity, interpretability, and the prevention of misinterpretations [18; 19; 20; 21]. The choice of color scale can significantly affect the perceived magnitude and patterns within a dataset, potentially leading to disparities in comprehension. Moreover, the exploration of diverging, sequential, or categorical colormaps adds complexity to the investigation, as each type may excel or fail in different contexts [22]. When dealing with the optical properties of clinical data, more often than not, samples are analyzed with several imaging modalities, which can help to better understand the physiology of different pathologies. However, such imaging devices may yield data with different spatial properties. In such cases, one could provide numerous false-color images for the various parameters, each of which would be better viewed in a different colormap according to their characteristics. Nonetheless, evaluating multiple color scales for the same sample may complicate the interpretability of the dataset or even worsen it [23]. Some imaging modalities, such as polarization imaging, combine different parameters using the hue-saturation-value (HSV) color space, assigning different optical properties to each component in terms of the existing relationship between the physical parameters and the interpretation of the H, S, and V coordinates, but the correction of such parameters to enable better visualization is based on the user input [24].