No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception

• Published in: IEEE transactions on circuits and systems for video technology 2014-04, Vol.24 (4), p.591-602
• Main Authors: Ryu, Seungchul, Sohn, Kwanghoon
• Format: Article
• Language: English
• Subjects: Advertising campaigns; Applied sciences; Artificial intelligence; binocular quality perception model; Biological and medical sciences; Computer science; control theory; systems; Exact sciences and technology; Eye and associated structures. Visual pathways and centers. Vision; Fundamental and applied biological sciences. Psychology; Hypotheses; Image color analysis; Image quality; Information systems. Data bases; Mathematical model; Measurement; Memory organisation. Data processing; noreference; objective quality metric; Pattern recognition. Digital image processing. Computational geometry; Quality assessment; Rule of origin; Software; Stereo image processing; stereoscopic image; Vertebrates: nervous system and sense organs; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2013.2279971

Quality perception of 3-D images is one of the most important parameters for accelerating advances in 3-D imaging fields. Despite active research in recent years for understanding the quality perception of 3-D images, binocular quality perception of asymmetric distortions in stereoscopic images is not thoroughly comprehended. In this paper, we explore the relationship between the perceptual quality of stereoscopic images and visual information, and introduce a model for binocular quality perception. Based on this binocular quality perception model, a no-reference quality metric for stereoscopic images is proposed. The proposed metric is a top-down method modeling the binocular quality perception of the human visual system in the context of blurriness and blockiness. Perceptual blurriness and blockiness scores of left and right images were computed using local blurriness, blockiness, and visual saliency information and then combined into an overall quality index using the binocular quality perception model. Experiments for image and video databases show that the proposed metric provides consistent correlations with subjective quality scores. The results also show that the proposed metric provides higher performance than existing full-reference methods even though the proposed method is a no-reference approach.