Cascade-CMAC neural network applications on the color scanner to printer calibration

Abstract

This paper presents an application of using a Cascade-CMAC (Cerebellar Model Articulation Controller) neural network to solve some color calibration problems, which include color differences induced from gamuts mis-match and the non-linear transformation characteristics between color scanning input devices and color printing output devices. For this purpose, we proposed a scalable learning architecture ''Cascade-CMAC'' to implement an adaptive color calibration system. By analyzing the preliminary learning situation, the scalable architecture can dynamically create a new learning unit to better represent a finer color resolution, so that the learning capacity as well as the color details of the system can be greatly improved. From the experimental results, the proposed Cascade-CMAC architecture can improve the rate of convergence and also can adjust the learning architecture effectively. The learning speed can be 2 similar to 4 times faster than the conventional CMAC. The effectiveness of this neural network has been tested by observing the differences between the calibrated and the un-calibrated output on a number of known samples. By using Macbeth Color-Checker which contains 24 color ptaches as benchmark, tile average color differences between the original and the calibrated print-out is improved from 15 Delta E-ab to 8 Delta E-ab under the 3 Delta E-ab convergent criterion for training. The calibration performance is somewhat significant.