Calligraphy appreciation is widely considered to require sequence restoration, while the sequence restoration of painting is ignored in long time. Moreover, the detail feature of brush strokes is considered to enhance the perception of sequentiality. In order to quantify the sequentiality perception and to explore the influence of detail features of strokes on sequentiality perception, a sequentiality perception quantization method based on Markov chain entropy rate was proposed. Firstly, the perceived sequentiality of an individual to the markers on the artwork was modeled as a Markov chain. Then, the entropy rate of the Markov model was calculated to measure the uncertainty of the perceived sequentiality. Finally, the negentropy was used to measure the order of the perceived sequentiality and was normalized to obtain the measurement index:sequentiality perception. The feasibility of this method was verified through the actual measurement of the sequentiality perception of a group of artworks. And based on the proposed sequentiality measurement, the effect of graph transformation on the sequentiality perception of artworks was studied. Experimental results show that the sequentiality consistency keeps high level when rotation or mirror transformation is performed to the original image, while the correctness varies significantly. This means that the feature of brush strokes is not the primary factor in forming the sequentiality experience, and the viewer's own experience of order of strokes is more important in the formation, but this conclusion needs to be further verified.

Current research generally focuses on the application of gamification to improve the engagement of learning. However, the research on gamification in specific fields such as color education is not sufficient, and there is a lack of analysis on the gamification elements and influence factors of learning effects. For these problems, a game model for training color recognition was designed. Firstly, two different ways of playing were designed with same core gameplay but different interaction modes. Then, the same virtual reward was added in both playing ways. Finally, the effects of two playing ways on learning effect with or without virtual reward were compared, and the effect of virtual reward in the same playing way were compared. The results show that gameplay design mainly affects learning efficiency, and virtual reward mainly affects engagement.

In order to create novel artistic effects, a period-dynamic-image model was proposed, in which each element is a periodic function. Instead of using an array of color pixels to represent a digital image, a Fourier model was used to represent a periodic dynamic image as an array of functional pixels, and the output of each pixel was computed by a Fourier synthesis process. Then three applications with three rendering styles were put forward, including dynamic painting, dynamic distortion effects and dynamic speech balloons, to visually display the periodic dynamic images. A prototype system was constructed and a series of experiments were performed. The results demonstrate that the proposed method can effectively explore the novel artistic effects of periodic dynamic images, and it can be used as a new art media.