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This case study proposes a novel network named inception network (InceptNet), which uses inception blocks as shown in Figure 5.6 and also used in GoogleLeNet [23]. The inception block consists of multiple parallel convolution blocks of different filter sizes. An extra 1 × 1 convolution before the 3 × 3 and 5 × 5 convolutions help in dimensionality reduction. These different filter sizes help in extracting both the generic features spread across the image and the local features. The encoder and decoder blocks of the proposed network are shown in Figure 5.7, which follows the autoencoder model presented in Figure 5.5.

Figure 5.6 Inception block.

Figure 5.7 Encoder and decoder blocks of InceptNet.

The input to the encoder is the channel matrix, and the output is a vector of reduced dimension K. The encoder consists of a single inception block. This output vector is fed back to the decoder where the decoder network with two parallel inception blocks recovers the channel matrix. The compression from N-dimensions of channel matrix to K-dimensional vector is given in terms of compression ratio (CR), where CR = K/N.