In recent years, PCs have become very popular for a wide range of applications, such as immersive virtual reality scenarios. As a consequence, in the last couple of years, there has been a great effort to develop novel acquisition, representation, compression, and transmission solutions for PC contents in the research community. In particular, the development of objective quality assessment methods that are able to predict the perceptual quality of PCs. In this paper, we present an effective novel method for assessing the quality of PCs, which is based on descriptors that extract perceptual color distance-based texture information of PC contents, called Perceptual Color Distance Patterns (PCDP). In this framework, the statistics of the extracted information are used to model the PC visual quality. Experimental results show that the proposed framework exhibit good and robust performance when compared with several state-of-the-art point cloud quality assessment (PCQA) methods.