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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. Index Terms-stereoscopic image quality assessment, retinal ganglion cell, lateral geniculate nucleus, segmented stacked autoencoders, edge quality, color quality. I. INTRODUCTION 3 D visual content has penetrated our lives deeply. We can easily find 3D movies, 3D TVs, 3D digital cameras and mobile phones equipped with dual cameras around us. Through them countless stereoscopic images are produced everyday. These images often suffer from perceptual quality degradation caused by distortions when they are transmitted, stored, compressed and processed. The degraded images need to be restored and SIQA indices can provide a criterion for restoration [1], [2]. Image quality assessment (IQA) models are divided into three categories according to usage of the original image: full-reference (FR) [3]-[12], reduced-reference

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The no-reference (NR) quality assessment for stereoscopic images plays a significant role in 3D technology, but it also faces great challenges. In this paper, a novel NR stereo image quality assessment (SIQA) method is proposed. Based on the human visual system, this method mimics the summation and difference channels, which consider the binocular interactive perception property, to process the visual information. Especially, the summation and difference images are calculated by the contrast of hue and luminance in color patches. Meanwhile, considering the interactive filtering between the left and right viewpoints, this method uses the filtered information as the weighting factor to integrate the visual information of the summation and difference channels to form the summation-difference mapping image (SDMI). Then, energy entropy, bivariate generalized Gaussian distribution for the joint distribution of SDMI and the depth map subband coefficients, and the local log-Euclidean multivariate Gaussian descriptor are detected as the feature descriptors. Support vector regression, trained by the features, is utilized to predict the quality of stereoscopic images. Experimental results demonstrate that the proposed algorithm achieves high consistency with subjective assessment on four SIQA databases.