Quantifying grain size and grain shape of natural sediments is an important aspect of studying provenance material composition, weathering and denuded environment, dynamic conditions of transport and deposition. There are many testing techniques and data processing methods based on different principles. The image method evaluates the size and grain shape characteristics of the overall grains by the measurement of a large number of rains, and is the most intuitive expression of the size and shape characteristics of the sample’s grains. Based on the data of both dynamic image and laser size measurements, the mean size (Mz), sorting coefficient (Sd), skewness (Sk), kurtosis (Kg) of samples are calculated using both moment and graphic methods; and then the relationship between the grain shape and grain size obtained by image method were discussed. The main conclusions are as follows. (a) The Mz values of the two calculations are almost identical, especially for image method, and Sd values of the two calculations is strongly correlated. The graphic Sk and Kg approximate with the moment’s ones for the samples with near-normal distributions. however, deviates significantly from the moment’s value for non-normal samples. So, the graphic calculation cannot reflect the real skewness and kurtosis of the samples. (b) There exists a moderate correlation between Mz values of both image and laser measurements; but the Sd, Sk and Kg parameters from laser measurement are almost unrelated to those from image one. (c) There are significant differences between the grain-size distributions, including peak positions, height, and shapes of peaks, obtained from two measurements. Different from the previous view that image’s Mz is generally coarser than laser’s one, this study found that the laser’s Mz is coarser than the image one for some sediment, which, perhaps, is mainly related to the multi-mineral properties and multi-shape irregularities of the sample. (4). Diameter of the circle of equal projection area(EQPC)-spherical two-dimensional density distributions can be used to distinguish different grain clusters within the sediment, each clsuster may also have different planar spread patterns. Combined with morphological parameters, granular information can provide new potential and opportunities for deposition environment analysis.