In this paper, we propose simple and effective compression of CSLBP (Center Symmetric Local Binary Pattern) descriptors, which is a textured based operator and mostly used as key point descriptor. With default parameters for computation, it is 256-length descriptor for each keypoint or affine patch. CSLBP is an extended form of LBP (Local Binary Patterns). The calculation of CSLBP descriptor is effective, robust, and straightforward for different image transformations for instance; image blurring and illumination alteration. However, an improvement in time and space consumption of CSLBP can be attained by means of simple compression. For this reason, CSLBP is a smart choice for smart phones as well as large databases. We reduce the descriptor length (dimensions) upto 50% without applying any techniques of dimensionality reduction like PCA (Principle Component Analysis) or LDA (Linear Discriminant Analysis). The compressed CSLBP descriptor is denoted as C-CSLBP. The performance of C-CSLBP is evaluated on state-of-the-art datasets using standard metrics. It is quantitatively shown by experiments that C-CSLBP is equivalently effective compared to CSLBP despite of reduced dimensions