Perceived Image Quality Assessment for Stereoscopic Vision

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Akhter, Roushain
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This thesis describes an automatic evaluation approach for estimating the quality of stereo displays and vision systems using image features. The method is inspired by the human visual system. Display of stereo images is widely used to enhance the viewing experience of three-dimensional (3D) visual displays and communication systems. Applications are numerous and range from entertainment to more specialized applications such as: 3D visualization and broadcasting, robot tele-operation, object recognition, body exploration, 3D teleconferencing, and therapeutic purposes. Consequently, perceived image quality is important for assessing the performance of 3D imaging applications. There is no doubt that subjective testing (i.e., asking human viewers to rank the quality of stereo images) is the most accurate method for quality evaluation. It reflects true human perception. However, these assessments are time consuming and expensive. Furthermore, they cannot be done in real time. Therefore, the goal of this research is to develop an objective quality evaluation methods computational models that can automatically predict perceived image quality) correlating well with subjective predictions that are required in the field of quality assessment. I believe that the perceived distortion and disparity of any stereoscopic display are strongly dependent on local features, such as edge (non-uniform) and non-edge (uniform) areas. Therefore, in this research, I propose a No-Reference (NR) objective quality assessment for coded stereoscopic images based on segmented local features of artifacts and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness, blur, and the zero-crossing within the block of images, are evaluated in this method. A block-based edge dissimilarity approach is used for disparity estimation. I use the Toyama stereo images database to evaluate the performance and to compare it with other approaches both qualitatively and quantitatively.
Image Quality Assessment, Stereoscopic Images, 3D Computer Vision, Compressed Image Artifacts
Roushain Akhter (2010). No-reference stereoscopic image quality assessment. In Proceedings of SPIE: Stereoscopic Displays and Applications XXI, volume 7524