Images synthesized using Depth-Image-Based Rendering (DIBR) techniques are characterized by complex structural distortion. Multi-resolution multi-scale sparse image representation generated using morphological Difference of Closings operator (DoC) is used to efficiently capture structure-related distortion of synthesized images in the noreference DoC-GRNN image quality assessment model. Nonlinear morphological Difference of Closings operator (DoC) with an array of line-shaped structuring elements of increasing length is used to extract perceptually important details of object structure at different scales and resolutions. The sparsity of DoC band is calculated as scalar feature. The extracted features are mapped to the quality score by general regression neural network (GRNN). We have explored the influence of the direction of an array of line-shaped structuring elements on the model's performances. The DoC-GRNN model shows high agreement with perceptual quality scores, comparable to the state-of-the-art metrics, when evaluated on the stereoscopic DIBR-synthesized images of MCL-3D dataset.