Compressive sensing is an emerging technology which can recover a K-sparse signal vector from M = O(Klog(K=N)) measurements. However, it is a challenge to know exactly how many measurements an image requires to achieve an acceptable recovered visual quality. In this paper, we study the relationship between the image's complexity and its sparsity. We propose a mathematical model to estimate the number of needed measurements by using the image's texture, the edge density and the target reconstruction quality. There exists a linear function between them. The experimental results with a large number of photo pictures show that, quite most reconstructed images using our pre-calculated number of measurements have good enough quality, which confirms our proposed image-complexity-based model well.