Investigating the possibility of using fewer training samples — in the color prediction model based on CIEXYZ using an effective coverage map

Yuanyuan Qu, Sasan Gooran
Linköping University (Sweden)
Download paper

Play (17min)

Download: MP4 | MP3

The goal of the present work is to reduce the number of the training samples used in our color prediction model based on CIEXYZ using an Effective Coverage Map while keeping satisfying prediction. A general approach is proposed in this paper to choose the best reference combination for the training samples. The approach is based on the dot gain behavior of each primary ink, which is characterized by three curves using CIEXYZ tri-stimulus values. The proposed approach is built in our model to predict the color values for the color prints using two different devices, i.e. a laser printer and an inkjet printer. For the laser printer the number of the training samples is reduced from 125 to 64 while still giving quite good result. The approach also shows that for the test laser printer it is possible to further cut this number to 53 with a satisfying result. For the inkjet printer the number of training samples for our model is reduced from 125 to 79 or 64, both giving satisfying results.

You may also like:

  1. Color Image Super Resolution: A Two-Step Approach Based on Geometric Grouplets
  2. Spectral Image Prediction of Color Halftone Prints Based on Neugebauer Modified Spectral Reflection Image Model
  3. An expanded Neugebauer formula, using varying micro-reflectance of the Neugebauer primaries
  4. Investigating human color harmony preferences using unsupervised machine learning
  5. Feature based no-reference continuous video quality prediction model for coded stereo video

  • Share
1 Star2 Stars3 Stars4 Stars5 Stars (No Ratings Yet)
Loading ... Loading ...