Local Gray Component Replacement Using Image Analysis

In printing, ink is one of the most important cost factors, which accounts for approximately 25-30% of the cost per page; therefore, reducing ink consumption is of great interest. The traditional approach to this problem is to modify the ICC profile to increase the use of black ink instead of the combination of cyan, yellow, and magenta; this approach is known as the gray component replacement (GCR). While this strategy reduces ink consumption, it often results in visually grainy images in otherwise smooth regions, and is therefore of limited use or even unacceptable for many applications, such as photoprinting. In this work, we propose a novel, context sensitive and spatially variant GCR method, which yields ink consumption figures that are similar to an aggressive GCR, but in contrast produces perfectly acceptable print quality results. Our approach is based on the visual masking effect: image areas with high activity level, such as high contrast textures, mask the increased graininess, and other inaccuracies such as (small) color shifts. Therefore, we propose to dynamically vary the amount of gray replacement across the image as a function of the local “activity” of the image. In lighter, smoother regions, less aggressive GCR is applied, and the image quality is preserved, while in more active regions where the change is not visible, more aggressive GCR is applied. The performance of the proposed method is tested on images randomly chosen from several photo collections. The initial results indicate about 15% reduction in overall ink consumption with perfectly acceptable print quality.