Dynamic RGB-to-CMYK conversion using visual contrast optimisation

As the standard colour space used by printers, Cyan, Magenta, Yellow, Black (CMYK) colour model is a subtractive colour space used to describe the printing process. Existing CMYK conversion methods rely on static conversion table, which may not preserve the subtle visual structures of images, due to the local visual contrast loss caused by the static colour mapping. Therefore, the authors propose a novel dynamic Red, Green, Blue (RGB)-to-CMYK colour conversion, which utilises the weighted entropy to extract the pixels with filter response change dramatically. They obtain the image activity map by combining these pixels with high skin probability regions, and optimise the colour conversion of each pixel to ensure that the ink used for each pixel can be saved, while the visual contrast can be preserved with ink-saving. In this way, their proposed technique can achieve dynamic CMYK colour conversion, in which the consumption of ink can be reduced without the loss of visual contrast. The experimental results have shown that their dynamic CMYK colour conversion saved 10-25% ink consumption compared with the static conversion method, while with high visual quality for the converted images.

[1]  Tao Xu,et al.  Pixel-wise skin colour detection based on flexible neural tree , 2013, IET Image Process..

[2]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Yong Zhang,et al.  A Skin Color Model Based on Modified GLHS Space for Face Detection , 2014, J. Inf. Hiding Multim. Signal Process..

[4]  Chih-Hung Chang,et al.  On the structure of multi-layer cellular neural networks , 2012 .

[5]  Yi Wan,et al.  A Novel Framework for Optimal RGB to Grayscale Image Conversion , 2016, 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC).

[6]  Minh N. Do,et al.  Fast Global Image Smoothing Based on Weighted Least Squares , 2014, IEEE Transactions on Image Processing.

[7]  Jakub Nalepa,et al.  Skin Detection and Segmentation in Color Images , 2014 .

[8]  Jin Young Choi,et al.  Gradient preserving RGB-to-gray conversion using random forest , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[9]  Keiichi Uchimura,et al.  Scale-Space Processing Using Polynomial Representations , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  T. Kuiken,et al.  A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis , 2016, IEEE Journal of Translational Engineering in Health and Medicine.

[11]  Sunita Dhariwal,et al.  Comparative Analysis of Various Image Enhancement Techniques , 2011 .

[12]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Deepak Ghimire,et al.  Nonlinear transfer function-based local approach for color image enhancement , 2011, IEEE Transactions on Consumer Electronics.

[14]  Wei Zhang,et al.  A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors , 2015 .

[15]  Mohamed Cheriet,et al.  Influence of Color-to-Gray Conversion on the Performance of Document Image Binarization: Toward a Novel Optimization Problem , 2015, IEEE Transactions on Image Processing.

[16]  Rynson W. H. Lau,et al.  Saliency-Guided Color-to-Gray Conversion Using Region-Based Optimization , 2015, IEEE Transactions on Image Processing.

[17]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.