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A blind stereoscopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route

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journal contribution
posted on 01.11.2018 by Jiachen Yang, Kyohoon Sim, Xinbo Gao, Wen Lu, Qinggang Meng, Baihua Li
Most of the current blind stereoscopic image quality assessment (SIQA) algorithms cannot show reliable accuracy. One reason is that they do not have the deep architectures and the other reason is that they are designed on the relatively weak biological basis, compared with findings on human visual system (HVS). In this paper, we propose a Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) based on the whole visual perception route from eyes to the frontal lobe, and especially focus on edge and color signal processing in retinal ganglion cells (RGC) and lateral geniculate nucleus (LGN). Furthermore, to model the complex and deep structure of the visual cortex, Segmented Stacked Auto-encoder (S-SAE) is used, which has not utilized for SIQA before. The utilization of the S-SAE complements weakness of deep learning-based SIQA metrics that require a very long training time. Experiments are conducted on popular SIQA databases, and the superiority of DECOSINE in terms of prediction accuracy and monotonicity is proved. The experimental results show that our model about the whole visual perception route and utilization of S-SAE are effective for SIQA.


This work was partially supported by National Natural Science Foundation of China (No. 61471260), Natural Science Foundation of Tianjin (No. 16JCYBJC16000), and the Foundation of Pre-Research on Equipment of China (NO.61403120103).



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  • Computer Science

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IEEE Transactions on Image Processing


YANG, J. ... et al, 2018. A blind stereoscopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route. IEEE Transactions on Image Processing, 28 (3), pp.1314-1328.


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