Classification of Chroma Reconstruction Method by Machine Learning Method
Meng-Hsuan Kuo, Yuchen Shen, Yih‐Shyh Chiou, Shih‐Lun Chen, Ting-Lan Lin · 2020
In this paper, we propose a method to predict subsampling scheme by using the machine learning for the chroma reconstruction of screen content images (SCIs). We create a feature matrix with thirty features, and use the classification learner, error-correcting output codes (ECOC) classifier for multiclass learning, to train the model. After testing through the model, we finally get the experimental data that shows us the correlation between the luma and chroma and the accuracy of the model. The accuracy of the model is up to 92%, which provides the decoder with an accurate subsampling scheme. Therefore, with the correct subsampling scheme, it allows the subsampled chroma to be reconstructed accurately.