## 4 Experimental Results and Discussion
### Training and Testing Data  
Considering that the appropriate amount of training data can increase the CNN performance [29], we used 20 stereoscopic images from the Middlebury Stereo Dataset [30] as the training dataset. Each image pair was comprised of two views. Figure 5 shows the right view of each pair. To provide sufficient information for training the modified CNN, while reducing the complexity of the modified CNN, we extracted the view difference image between the 20 pairs of the FR left view image and the interpolated right view image, and we randomly cropped sub-images with the size of 33 \(\times\) 33 from the training images. A total of 552,729 sub-images (that is \(N\), the number of training samples) for training were generated. To avoid the boundary effect in the training process, all the convolutional layers have no padding. Moreover, although we used a changeless image size during training, the modified CNN could be employed on images of variable sizes in the testing process.  
To test the performance and robustness of the modified CNN, we employed the first frame of various multi-view video sequences, including Champagne_tower, Dog, Pantomime, Newspaper, Pozan Street [31], Ballet, and Breakdancers [32], as well as the remaining image pairs in the Middlebury  
Figure 5: Right view of each stereoscopic pair in the training dataset (from the Middlebury Stereo Dataset [30]).  
Stereo Dataset; Sword 2, Umbrella, and Vintage. Figure 6 shows the right view of each pair in the testing dataset. For Champagne_tower, the FR left view is view 38 and view 39 is selected as the LR right view. For Dog and Pantomime, the FR left view is view 40 and view 39 is selected as the LR right view. For Newspaper, the FR left view is view 2 and view 3 is selected as the LR right view. For Poznan Street, the FR left view is view 5 and view 4 is selected as the LR right view. For Ballet and Breakdancers, the FR left view is view 2 and view 1 is selected as the LR right view.  
In this paper, the specific CNN parameters are established according to practical experience, as shown in Figure 4. The MATLAB toolbox MatConvNet [33] is applied to actualize our CNN. Each parameter of the convolutional filters is set to \(\left[f_{l},f_{l},c_{l},n_{l}\right]_{l=1}^{3}=\left[9,9,1,64\right]_{l=1}, \left[1,1,64,32\right]_{l=2},\left[5,5,32,1\right]_{l=3}\). To strengthen the correlation between image patches, the convolution stride is set to one for all layers. For a three-channel color image, we followed the methods of [12; 13; 14; 15] in the experiments. Additionally, our stereoscopic image SR is used to contribute to the MR stereoscopic video coding model, whose video format is YCbCr (That is a color space. Y is the brightness (luma) component of the color space, while Cb and Cr are the blue and red concentration offset components). Thus, the color image is converted to the YCbCr color space, and only the Y component of the image is reconstructed by our SR reconstruction method, while both the Cb and Cr components are upsampled by the bicubic interpolation method [34]. Furthermore, although our network can be easily extended to multi-channel image processing, [PERSON]'s experiments performed on the YCbCr color space [15] demonstrated that the Cb and Cr channels scarcely improved the performance. Therefore, the following objective evaluation indices are calculated only in the Y channel in Sections 4.2 and 4.3.