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Deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and the Depth Image-Based Rendering (DIBR) process in a multi-view video system. Due to the lack of Multi-view Video plus Depth (MVD) data, a deep learning-based model using more synthetic Synthesized View Images (SVI) is proposed, in which a random irregular polygon-based SVI synthesis method is proposed to simulate the DIBR distortion based on existing massive RGB/RGBD data. In addition, the DIBR distortion mask prediction network is embedded to further enhance the performance.

quality enhancement
synthetic images
data augmentation
3D video system
DIBR

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Update Time:
22 Nov 2022

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$${I}_{syn}=(\mathit{1}-M)\odot I+{\displaystyle \frac{M\odot (I+{I}_{\delta})}{2}},$$

where denotes the synthetic SVI, I denotes the compressed captured view images,**1** denotes the matrix with all elements as 1, M denotes the mask area corresponding to the detected strong depth edges, ⊙ denotes dot product, and denotes the images added with random noise, i.e., Gaussian noise, speckle noise, or the patch-shuffled version of I. It could be observed that synthesized by Gaussian noise and speckle noise is not very visually resembling synthesis distortion, and synthesized by patch-based noise exhibits similar behaviors a little in the way that the pixels in a local patch appear as disorderly and irregular.

where denotes the synthetic SVI, I denotes the compressed captured view images,

SVI with DIBR distortion can be viewed as the tiny movement of textures within random polygon area along the depth transition area. To better simulate the irregular geometric distortion, a simple random polygon generation method which could control irregularity and spikiness will be introduced as follows. A random polygon generation method could be found in ^{[6]}. Following the method ^{[7]}, to generate a random polygon, a random set of points with angularly sorted order would be first generated; then, the vertices would be connected based on the order. First, given a center point P, a group of points would be sampled on a circle around point P. Random noise is added by varying the angular spacing between sequential points and the radial distance of each point from the center. The process can be formulated as
where and represent the angle and radius between the i-th point and assumed center point, respectively. denotes the random variable controlling angular space between sequential points, which is subject to a uniform distribution featured by the smallest value and largest value , where n denotes the number of vertices. Moreover, is subject to Gaussian distribution with a given radius R as mean value and as the variance. R could be used to adjust the magnitude of the generated polygon. could be used to adjust the irregularity of the generated polygon by controlling the angular variance degree through the interval size of U. could be used to adjust the spikiness of the generated polygon by controlling the radius variance through the normal distribution. Large and indicates strong irregularity and spikiness, and vice versa, which can be shown in **Figure 3**.

$$\left\{\begin{array}{c}{\theta}_{i}={\theta}_{i-1}+{\displaystyle \frac{1}{k}}\u25b5{\theta}_{i}\hfill \\ \u25b5{\theta}_{i}=U({\displaystyle \frac{2\pi}{n}}-\u03f5,{\displaystyle \frac{2\pi}{n}}+\u03f5),\hfill \\ k=\sum \u25b5{\theta}_{i}/\pi \hfill \\ ri=clip\left(N\right(R,),0,R)\hfill \end{array}\right.$$

Thus, the synthetic SVI composed by the proposed random polygon noise can be obtained as
where denotes the vertices set located in a local region generated by the random polygon method, denotes a random vector for all points of to be bodily shifted in . is fused with I in the strong depth regions. In **Figure 2**f,j, it can be observed that the DIBR distortion generated by the activity of textures within random polygon area along the edges resembles the distortion visually.

$$\left\{\begin{array}{c}{I}_{syn}=(\mathit{1}-M)\odot I+{\displaystyle \frac{M\odot (I+{I}_{sh})}{2}},\\ {I}_{sh}\left(\psi \right)=I(\psi +\eta )\hfill \end{array}\right.$$

Existing IQA models for SVI demonstrate that DIBR distortion position determination is the key procedure for quality assessment ^{[8]}^{[9]}, which hints that knowing and paying more attention to DIBR distortion position may elevate SVQE models in enhancing SVI quality. Therefore, how to incorporate the DIBR distortion position into SVQE models becomes a new issue. The intuitive way is directly integrating DIBR distortion position with distorted image as a whole input. **Figure 4**a shows the sketch map of this way. It could be validated that knowing DIBR distortion position is helpful for synthesized image quality enhancement. However, the ground truth DIBR distortion position is often not known, so the position has to be detected or estimated. Inspired by de-raining ^{[10]} and shadow removal ^{[11]}^{[12]}, SVI quality enhancement could be regarded as two tasks, i.e., DIBR distortion mask estimation and image restoration/denoising. Reviewing these works, there are three main possible ways to group mask estimation and image restoration task network, i.e., successive (series) network, parallel network (multi-task), parallel interactive network. The sketch map of these ways is demonstrated in **Figure 4**b–d. In addition to different organization or design of networks, attention mechanism, such as spatial attention ^{[13]}, self-attention ^{[14]}, or non-local attention ^{[15]}, is also considered in existing denoising or restoration networks. Researchers mainly focus on networks which explicitly combine the DIBR mask prediction and DIBR distortion elimination and mainly test the successive (series) network shown in **Figure 4**b.

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Zhang, H.; Cao, J.; Zheng, D.; Yao, X.; Ling, B.W. DIBR Distortion Mask Prediction Using Synthetic Images. Encyclopedia. Available online: https://encyclopedia.pub/entry/35737 (accessed on 27 November 2022).

Zhang H, Cao J, Zheng D, Yao X, Ling BW. DIBR Distortion Mask Prediction Using Synthetic Images. Encyclopedia. Available at: https://encyclopedia.pub/entry/35737. Accessed November 27, 2022.

Zhang, Huan, Jiangzhong Cao, Dongsheng Zheng, Ximei Yao, Bingo Wing-Kuen Ling. "DIBR Distortion Mask Prediction Using Synthetic Images," *Encyclopedia*, https://encyclopedia.pub/entry/35737 (accessed November 27, 2022).

Zhang, H., Cao, J., Zheng, D., Yao, X., & Ling, B.W. (2022, November 22). DIBR Distortion Mask Prediction Using Synthetic Images. In *Encyclopedia*. https://encyclopedia.pub/entry/35737

Zhang, Huan, et al. ''DIBR Distortion Mask Prediction Using Synthetic Images.'' *Encyclopedia*. Web. 22 November, 2022.

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