Scanner RGB Research Articles

A Scan-Digitizing Algorithm of the Printed Original Based on Use of Near-Neutral Scales

Scan-digitizing is one of the main ways to digitize the original. Building scanner profiles are usual practice to get the consistent color. In the paper, we describe how to get the best possible ‘Raw RGB’ image from a scanner. First we measured the accuracy of those different input profiles. The profile accuracy depends on the relationship between scanner RGB and the corresponding LAB or XYZ values. Then we compared the range of those input profile tones. The ideal tone response curve can be decided by setting the scanner gamma. So a good input profile can faithfully render the dark and murky appearance of an underexposed image or the washed-out appearance of an overexposed one. In order to get a good scanner profile we proposed a method to determine a suitable tone response curve. A new algorithm was then developed according to N-component LUT-based input profile. Experiments were carried out to compare these scanner profiles. The results showed that the ICC profile generated by the new algorithm has obvious advantages in accuracy and grey predicting.

Evaluating Textile Fabric Color Variation by Scanner

The purpose of this publication is to describe the possibility of using a scanner for the evaluation of color variation or color difference of textile fabrics. Initially, the color specification of colored fabrics were measured by spectrophotometers and the actual color differences between colored fabrics were calculated by the δE*ab color difference formula. Then, a scanner was used to take images of the textile fabrics. The obtained images were filtered for noise removal. The RGB values of the obtained images were used for the evaluation of textile fabrics color variation. Several methods were used to evaluate color difference by a scanner. The best prediction was obtained by the neural networks methodwith 1.014 δE*ab. Using this method, the accuracy of prediction for training sets was better than that for the testing sets. The performance of each method depends on the color difference values. so that, for low and high color difference, the best prediction was obtained by neural network, and for median color difference, the best prediction was obtained by the multi linear regression method. The obtained results show that the color variation of textile fabrics can be estimated by using scanner RGB variation.

Scanner characterization for color measurement and diagnostics

We propose a novel scanner characterization approach for applications requiring color measurement of hardcopy output in calibration, characterization, and diagnostics applications. The method is advantageous for common practical color printing sys- tems that use more than the minimum of three colorants necessary for subtractive color reproduction; printing with cyan (C), magenta (M), yellow (Y), and black (K) is the most prevalent example we use in our description. The proposed method exploits the fact that for the scenarios in consideration, in addition to the scanner RGB values for a scanned patch, the CMYK control values used to print the patch are also available and can be exploited in characterization. An indexed family of 3D scanner characterizations is created, each characterization providing a mapping from scanner RGB to CIELAB for a fixed value of K, the latter constituting the index for the char- acterization. Combined together, the family of 3D characterizations provides a single 4D characterization that maps scanner RGB ob- tained from scanning a patch and the K control value used for print- ing the patch to a colorimetric CIELAB measurement for the patch. A significant improvement in the robustness of the method to varia- tions in printing is obtained by modifying the K index to utilize the scanned output for a black-only patch printed with the correspond- ing K value instead of directly utilizing the control K value used at the printer. Results show that the proposed 4D scanner character- ization technique can significantly outperform standard 3D ap- proaches in the target applications. © 2007 SPIE and IS&T.