Strategies of Applying Visual Element Combination to Improve Visual Cognitive Efficiency in the Era of Big Data Network

Abstract

Fundamentally, visual cognition refers to a way of thinking in which the objective information of the external world is transmitted to the brain through the visual nerve and then processed by the brain. In the evolutionary history of human beings, the pressure brought by the environment has led to the rapid development of the visual system and visual cognition. As with language ability and logical ability, the understanding of visual cognition is the key to the analysis of human intelligence. Therefore, research on improving the efficiency of visual cognition is urgently needed. The purpose of this paper is to find methods and strategies to improve the efficiency of visual cognition based on the application of visual element combination. This article first gives a general introduction to visual elements and design methods. Then, the eye movement behavior and visual saliency calculation model are established. Using Gaussian analysis and channel saliency test method based on “where” and “what” principles, the cognitive effect of pictures in visual elements is specifically analyzed. Then, the questionnaire survey method was used to conduct experiments on the problem that visual elements affect the efficiency of visual cognition, and finally the results were obtained. At the macro level, dynamic pictures with color and auxiliary text can effectively improve the cognitive efficiency of vision. It enables people to grasp cognitive objects quickly and form stronger cognitive ability. However, at the micro level, due to the intuitiveness and meaning of words, the visual cognitive efficiency of the recognition effect is high. In terms of image scale, the two scale parameters [4 × 4] and [6 × 6] perform better. The human eye is relatively optimal at 4/90∼6/90 on the attention scale of the image. At the same time, under the [4 × 4] scale parameter, even if the image loses some features, it can save about 95% of the cognitive time overhead.