  Just Noticeable Difference (JND) has many applications in multimedia signal
processing, especially for visual data processing up to date. It's generally
defined as the minimum visual content changes that the human can perspective,
which has been studied for decades. However, most of the existing methods only
focus on the luminance component of JND modelling and simply regard chrominance
components as scaled versions of luminance. In this paper, we propose a JND
model to generate the JND by taking the characteristics of full RGB channels
into account, termed as the RGB-JND. To this end, an RGB-JND-NET is proposed,
where the visual content in full RGB channels is used to extract features for
JND generation. To supervise the JND generation, an adaptive image quality
assessment combination (AIC) is developed. Besides, the RDB-JND-NET also takes
the visual attention into account by automatically mining the underlying
relationship between visual attention and the JND, which is further used to
constrain the JND spatial distribution. To the best of our knowledge, this is
the first work on careful investigation of JND modelling for full-color space.
Experimental results demonstrate that the RGB-JND-NET model outperforms the
relevant state-of-the-art JND models. Besides, the JND of the red and blue
channels are larger than that of the green one according to the experimental
results of the proposed model, which demonstrates that more changes can be
tolerated in the red and blue channels, in line with the well-known fact that
the human visual system is more sensitive to the green channel in comparison
with the red and blue ones.
