Local Dimming for Video Based on an Improved Surrogate Model Assisted Evolutionary Algorithm

Abstract

Compared with the traditional liquid crystal displays (LCD) systems, the local dimming systems can obtain higher display quality with lower power consumption. Considering local dimming of the static image as an optimization problem and solving it based on an evolutionary algorithm, a set of optimal backlight matrix can be obtained. However, the local dimming algorithm based on evolutionary algorithm is no longer applicable for the video sequences because the calculation is very time-consuming. This paper proposes a local dimming algorithm based on improved surrogate model assisted evolutional algorithm (ISAEA-LD). In this algorithm, the surrogate model assisted evolutionary algorithm is applied to solve the local dimming problem of the video sequences. The surrogate model is used to reduce the complexity of individual fitness evaluation of the evolutionary algorithm. Firstly, a surrogate model based on convolutional neural network is adopted to improve the accuracy of individual fitness evaluation of surrogate model. Secondly, the algorithm introduces the backlight update strategy based on the content correlation between the video sequences' adjacent frames and the model transfer strategy based on transfer learning to improve the efficiency of the algorithm. Experimental results show that the proposed ISAEA-LD algorithm can obtain better visual quality and higher algorithm efficiency.