## 2 Proposed Method
\(\Omega\) indicates the whole spatial domain, \(\|\cdot\|_{2}\) denotes the \(l_{2}\) norm, and \(\langle\cdot\rangle\) represents the inner product. Subscript \"\(n\)\" is the index of the \(n\)-th training distorted S3D images. Subscripts \"\(\sigma\)\" and \"\(d\)\" are the original and distorted S3D images, respectively. _(3) Quality-predictive features extraction:_ Based on [PERSON]'s theory [24], local structural features based on the visual cortex (area V1) are related to image quality. LBPs are basic feature extractors that are commonly used to represent an image's structural and textural information [25]. First, we compute the rotation invariant uniform LBP map, \(LBP_{P,R,BRR,n}^{tri2}\), for BRR by comparing BRR intensity against its eight neighbors (in consideration of computational complexity, \(R\) and \(P\) were set to 1 and 8, respectively). To effectively capture textural and structural features, the BRR-weighted LBP histogram is applied to extract quality-predictive features of BRR, which are defined as  
\[h_{n}(k,\sigma)=\frac{1}{\Omega}\sum_{x,y}R_{d,n}(x,y,\sigma)\times f(LBP_{ P,R,BRR,n}^{tri2}(x,y,\sigma),k) \tag{5}\]where \(f(i,j)= \begin{cases}1,&\text{if }i=j\\ 0,&\text{otherwise}\end{cases} ,i\ \&\ j\in[0,P+1]\). _(4) BRR quality codebook:_ Thus far, we have gained a set of human-scored S3D images for training. All the quality scores (denoted by \([S_{n}]\)) and all the feature vectors (denoted by \([h_{n}(k,\sigma)]\)) of the distorted S3D images for training constitute a BRR quality codebook to reflect different quality levels of visual signals and associated quality-predictive features. _(5) Blind perceptual quality pooling:_ After obtaining a BRR quality codebook, we use the codes to predict the quality score of a test S3D image, relying on the hypothesis that S3D images with similar quality-aware features should share similar visual qualities. First, let \(l_{m}^{d}(k,\sigma)\) represent the quality-aware features of the \(n\)-th test S3D image. We define the distance \(H_{m,t}\) between \(l_{m}^{d}(k,\sigma)\) and \(h_{n}(k,\sigma)\) as the product of the chi-square distance between each pair of quality-predictive features. \[H_{m,n}[l_{m}^{d}(k,\sigma),h_{n}(k,\sigma)]=\sum_{n=1}^{N}\frac{\left(l_{m}^{ d}(k,\sigma)-h_{n}(k,\sigma)\right)^{2}}{l_{m}^{d}(k,\sigma)+h_{n}(k,\sigma)} \tag{6}\]  
where \(N\) is the total number of training S3D images. Let \(\{\hat{H}_{m,1},\hat{H}_{m,2},\cdots,\hat{H}_{m,9}\hat{H}_{m,10}\}\) represent the chi-square distances of 10 of the nearest quality-predictive features, which are calculated using the \(n\)-th test set and the selected training set. A distance-based weighting method is then utilized to measure the perceptual visual quality of the test set. The normalized weight for the 10 similarity scores of the selected training set, which is denoted by \(w_{m,n^{\prime}}\), can be represented as  
\[w_{m,n^{\prime}}=\left(\hat{H}_{m,n^{\prime}}\right)^{-1}/\sum_{n^{\prime}=1}^ {10}\left(\hat{H}_{m,n^{\prime}}\right)^{-1}. \tag{7}\]  
By distributing larger weights to the similarity values of the selected neighboring training S3D images, the final quality value of the \(n\)-th test S3D image is calculated as  
\[Q_{m}=\sum_{n^{\prime}=1}^{10}w_{m,n^{\prime}}\cdot S_{n^{\prime}}. \tag{8}\]