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C language programming is the most important professional core course of computer science. In the traditional teaching process, the teacher-oriented way seriously limits the role of this kind of courses, and cannot give full play to students’ initiative, especially for the computer science and technology specialty, we should take the construction of national first-class specialty as the goal, making engineering education certification as the opportunity, taking the modern industrial college as the carrier, promoting the all-round development of students as the foothold, and embodying the concept of “student-centered”. It is urgent to change the teaching methods and carry out comprehensive digital construction and reform. In this paper, the digital reform of C language programming is carried out in all aspects, such as preparation before class, teaching in class, expansion after class, practice teaching, curriculum design and subject competition. The author and the teachers of the course teaching team have written this article through their teaching reflection and practical experience in recent years, trying to briefly elaborate the innovative methods and achievements of the course in digital reform in recent years, so as to arouse everyone’s thinking and discussion, aiming at jointly promoting the development of this course and related majors.

Intraprediction is one of the most complex parts of High-Efficiency Video Coding (HEVC), because it selects the best prediction mode by calculating the cost of every Coding Unit (CU), which provides higher complexity of intracoding. Visual saliency map can show the attention regions of the human eyes, which is generated by certain static and space-time saliency detection method. By analyzing the percentage of coding time for different size CU, and the relation of visual saliency and CU depth, an intraprediction complexity control algorithm based on visual saliency is proposed in this paper. Based on the feature of the video and the target level, the saliency threshold is adapted to determine whether the current CU in the intraprediction processing should be split into smaller CUs or the division processing should be stopped early. Three samples were compared by the proposed algorithm and other algorithm, and the proposed algorithm has better performance in PSNR, BitRate, and coding time. Experimental results show that this algorithm can effectively control the coding complexity of intraprediction with minimal visual loss and can be applied to a number of scenarios, such as real-time video coding.