We consider the problem of quality assessment (QA) of image stitching algorithms used to generate panoramic images for virtual reality applications. Our contributions are two fold. We design the Indian Institute of Science Stitched Image QA (ISIQA) database consisting of 264 stitched images obtained by employing multiple stitching algorithms on images captured from 26 diverse scenes. The database has a wide perceptual quality spread and consists of a variety of artifacts due to stitching such as blur, ghosting, color and geometric distortions. We subjectively evaluate these images by acquiring 6600 human quality ratings by for these images viewed on a virtual reality head mounted device. We then devise an objective QA model called the Stitched Image Quality Evaluator (SIQE) using the statistics of steerable pyramid decompositions. In particular, we propose a Gaussian mixture model to capture the bivariate statistics of neighboring coefficients of steerable pyramid decompositions and show this to be effective in modeling the increased spatial correlation due to ghosting artifacts. An important characteristic of the proposed algorithm is that it requires no knowledge of the stitching algorithm used to obtain the panorama and only needs individual images and stitched image as input for predicting quality. We show through extensive experiments that our quality model outpeforms the exisitng No-Reference and Full-Reference QA models and correlates very well with subjective scores in the ISIQA database. The ISIQA database as well as the software release of SIQE have been made available online for public use and evaluation purposes.