67‐1: Distinguished Paper: Efficient Multi‐Quality Super Resolution Using a Deep Convolutional Neural Network for an FPGA Implementation

Abstract

We propose an efficient deep convolutional neural network for a super-resolution which is capable of multiple-quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.