Stereoscopic image quality prediction

Three-dimensional (3D) imaging has attracted considerable attention recently due to its increasingly wide range of applications. Consequently, perceived quality is a great important issue to assess the performance of all 3D imaging applications. Perceived distortion and depth of any stereoscopic images are strongly dependent on the local features, such as edge, flat and texture. In this paper, we propose an noreference (NR) perceptual quality assessment for JPEG coded stereoscopic images based on segmented local features of artifacts and disparity. The local features information of stereoscopic pair images such as edge, flat and texture areas and also the blockiness and zero crossing rate within the block of the images are evaluated for artifacts and disparity in this method. The result on our subjective stereoscopic images database indicates that the model performs quite well over a wide rang of image content and distortion levels.