Upsampling Algorithms for Autoencoder Segmentation Neural Networks: A Comparison Study This paper compares nine different upsampling methods used in convolutional neural networks in terms of accuracy and processing speed. The process of image segmentation using autoencoder neural networks consists of the image downsampling in the encoder and correspondingly of image upsampling in the decoder part of the network to achieve original image resolution. This paper focuses on the upsampling process in the decoder part of the standard UNet neural network. Three different interpolations are compared with and without subsequent lxl convolution layers and three transpose convolution layers for image upsampling using different size convolutional cores. The experiment has shown that the best practical results were achieved using simple nearest neighbor interpolation upsampling taking into consideration the computational time needed. The network using nearest neighbor interpolation upsampling achieved pixel accuracy of 99.47% and has shown fast training time and convergence in comparison with other networks using different upsampling methods. The data used in this work consist of a lumbar CT spine segmentation dataset.

Convolutional Neural Network Based UpSampling for Depth Video Intra Coding Depth video contains depth and disparity information of a scene, which is critical for 3D video systems. In this paper, a convolutional neural network (CNN) based block upsampling method is proposed to improve the efficiency of depth video intra coding. For each largest coding tree in a depth map, it is downsampled before sent into encoder and recovered into the original size in an intelligent way after lowresolution coding. A novel textureassisted CNN (TACNN) is presented to handle the depth block upsampling. The network is made up of several residual coding units and the features of texture block are extracted to assist the reconstruction of the corresponding depth block. Experimental results show that the proposed method achieves competitive ratedistortion performance compared with the stateoftheart approaches.