@INPROCEEDINGS{1511Verhack2017,
	AUTHOR = {Ruben Verhack and Simon Van de Keer and Glenn Van Wallendael and Peter Lambert and Thomas Sikora},
	TITLE = {Color prediction in image coding using Steered Mixture-of-Experts},
	BOOKTITLE = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017},
	YEAR = {2017},
	MONTH = mar,
	PUBLISHER = {IEEE},
	ADDRESS = {New Orleans, LA, USA},
	NOTE = {Electronic ISBN: 978-1-5090-4117-6
USB ISBN: 978-1-5090-4116-9
Print on Demand(PoD) ISBN: 978-1-5090-4118-3
Electronic ISSN: 2379-190X},
	DOI = {10.1109/ICASSP.2017.7952364},
	ABSTRACT = {We propose a novel approach for modeling and coding color in images and video. Luminance is linearly correlated with chrominance locally, as such we can predict color given the luma value. Using the Steered Mixture-of-Experts (SMoE) approach, the image is viewed as a stochastic process over 5 random variables including the 2-D pixel locations, 1 luminance and 2 chrominance values. We model this process as a continuous joint density function by fitting a K-modal 5-D Gaussian Mixture Model (GMM). As such, the chroma values are predicted as the expectation of the conditional density. To validate, the technique was integrated within JPEG showing PSNR gains in the lower bitrate regions. A deeper analysis of the tolerance of the activation function is given through recycling color models in video sequences, yielding a high quality reconstruction over a considerable range of frames.}
}