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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Stereoscopic Image Quality Assessment Based on Binocular Adding and Subtracting
verfasst von : Jiachen Yang, Bin Jiang, Chunqi Ji, Yinghao Zhu, Wen Lu
Erschienen in: Computer Vision
Verlag: Springer Singapore
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Abstract
There has been a great concern on blind image quality assessment in the field of 2D images, however, stereoscopic image quality assessment (SIQA) is still a challenging task. In this paper, we propose an efficient blind image quality assessment model for stereoscopic images according to binocular adding and subtracting channels. Different from other SIQA methods which focus on complex binocular visual properties, we simply use the visual information from adding and subtracting to describe binocularity (also known as ocular dominance) which is closely related to distortion types. To better evaluate the contribution of each channel in SIQA, a dynamic weighting is introduced according to local energy. Meanwhile, distortion-aware features based on wavelet transform are utilized to describe visual degradation. Experimental results on 3D image databases demonstrate the potential of the proposed framework in predicting stereoscopic image quality.