We introduce a novel workflow that will hopefully open new directions of processing and improvement in image reproduction. Existing gamut mapping algorithms can be classified into two basic categories: image-independent algorithms and image-dependent algorithms. The latter algorithms produce better reproduction; however, because they are time consuming and mathematically complex, the image-independent approach is commonly used in most imaging workflows. We suggest a new workflow that attempts to approach the image-dependent mapping method without incurring significant computational drawbacks nor requiring changes in the imaging industrial standards. The proposed method attempts to choose an appropriate gamut mapping per image without reconstructing the image gamut itself and without constructing an image-specific mapping on the fly, as required by image-dependent gamut mapping methods. Specifically, image characteristics are exploited for selection of a source gamut and a gamut mapping most appropriate for a given input image from a set of available mappings. Accordingly the proposed method is named image-guided gamut mapping. We show the practicability and advantages of the suggested workflow in several specific cases. We show that better image quality is achieved for 87% of the tested images when using the suggested workflow.