In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. Experimental results demonstrate that our affine motion estimation algorithm reduces encoding time by approximately 15%-35% while maintaining video bitrate and quality.

In China’s teaching reform, the ministry of education requires strengthening ideological and political education and puts forward the integration of Ideological and Political Elements (IPE) education into all courses. With the help of the student learning data management platform, we can accurately obtain students’ learning feedback information, analyze it, and explore the content of students’ ideological and political content. In this paper, we first propose a framework for mining ideological and political elements and discuss some strategies. Secondly, based on our three-year curriculum reform and construction, taking the computer algorithm course as an example, this paper introduces a case study in detail with the whole statement of the teaching process. Through our questionnaire survey, most students are highly satisfied with the meticulously designed integration of Ideological and political elements which can promote their interests in learning professional courses.

In order to meet the demand for blockchain talents, this paper explores the practical teaching of blockchain technology. Taking Ethereum as the platform and combining the application of blockchain technology in the field of digital copyright protection, we guide students to conduct research projects, so that students have a deep understanding of some abstract concepts of blockchain and master the methods of blockchain development.