Accurate Power Modelling Framework for Medical Images in Embedded System

Abstract

The display in embedded devices is a significant energy-consuming device, and the display content determines the degree of energy consumption. In the practice of energy saving and emission reduction, it is necessary to build an accurate medical image power prediction model for the display. Current power consumption prediction models for medical images often focus on the performance of a single prediction model, ignoring the ability of multiple single prediction models to improve performance. This paper proposes an accurate medical power framework (AMPF), which consists of three steps. In the first step, the strong predictor is used to compose multiple RGB single channels to obtain the power model. In the second step, the power between RGB channels is represented by errors, and the power generated by RGB channel dependence is obtained by the strong predictor method. The third step is to compose the power of the first step and the second step to get accurate medical image power prediction results. The experimental results show that the accuracy of the AMFP proposed in this paper reaches 0.9798, which is 6.836% higher than that of the single power model. The AMPF implemented by AdaBoost is superior to that implemented by EWM in performance and has about 1.3 times the time advantage in training.