The present research attempts to address an automated optimization-based image-embedding approach through a levels-directions decomposition framework. The subject behind the present approach is to design colour image watermarking with a focus on contourlet representation, whereas the watermarking intensity is accurately calculated via an optimization algorithm with constraint. In the approach presented here, a number of performance monitors for watermarked and logo images are realized to deal with a new fitness function in the aforementioned optimization algorithm. It is worth noting that the first performance monitoring is organized based on the peak signal-to-noise ratio and the structural similarity, whereas the second one is organized based on the normal correlation and the bit error rate, respectively. There is a scrambling module to represent the logo information in disorder, where a number of attacks are simultaneously applied to the watermarked image in order to adjust the appropriate value for watermarking intensity to realize a robust and efficient solution. The ability of a decision maker system is manually taken into account for choosing the best levels and the corresponding directions regarding the contourlet representation, and the investigated results are considered in a number of well-known colour space models including RGB, YIQ and YCbCr. The key contribution of the present research is made based on the new integration of a set of subsystems employed in colour space models under the embedding and de-embedding processes in the contourlet representation, and the watermarking intensity is acquired through the optimization algorithm with constraint to present the competitive outcomes with respect to state-of-the-art benchmarks. The procedure for extracting the information concerning the logo image from the processed watermarked image under a number of attacks is implemented through the approach proposed, whereas there is no information about the original image and the watermarking intensity to be processed.