Image Self-Coding Algorithm Based on IoT Perception Layer

Abstract

In fact, with the quick growth of IoT-related industries in recent years, multimedia contents such as digital image videos have also shown explosive growth. In the sensing layer of the three-layer IoT architecture, sensors are the most critical part, which mainly sense the state of the environment. In this paper, an image self-coding algorithm based on the IoT perception layer is proposed. There is no specific encoding algorithm for the pictures collected by the current Internet of Things network perception layer. This results in poor search results for the network images collected by the sensor at the perception layer. A deep convolutional neural network image self-coding algorithm based on the IoT perception layer combines prior knowledge with deep involutional aural networks to increase the discriminative ability of images while preserving the message of the images themselves and improving the goal of image search accuracy. The experimental results show that the block search algorithm is used for image registration to reduce energy consumption, the absolute difference sum algorithm is improved to improve the accuracy of image registration, and the progressively out weighted average algorithm is used to stitch the images. After image stitching, the communication volume with the base station is reduced, which can effectively reduce the network load by 60%.