Editorial for Special Issue of Journal of Big Data Research on “Big Data Meets Knowledge Graphs”

The purpose is to study how to innovate and teach TV programs in the background of big data. Shot boundary detection technology is adopted to search the content of TV programs video. The content retrieval of TV program is realized by shot boundary detection technology, which mainly includes two aspects of decompressed domain and compressed domain. Regarding the decompressed domain, a new abrupt shot change detection algorithm for decompressed domain is adopted to analyze of the whole search process of decompressed domain shot boundary. Regarding the compressed domain, the algorithm of video shot boundary detection on H.264/AVC code stream is used. Experimental results show that shot detection algorithm can detect not only abrupt shot change, but also gradual change. In the experiment, the comprehensive detection performance of various frequency sequences achieves 94% recall and 93.2% accuracy. The recall rate of abrupt shot change detection algorithm for experimental data is 94.5%, and the accuracy rate is 97.6%, which is superior to the detection performance of existing abrupt shot detection methods, and has a certain application value. Meanwhile, the similar video fast retrieval algorithm, the MinHash algorithm and LSH (Locality Sensitive Hashing) algorithm are compared. Similar video fast retrieval algorithm can achieve fast clustering of similar video faster, and can effectively retrieve similar video, so as to complete the fast retrieval of large-scale video data. The use of new abrupt shot change detection algorithm for decompressed domain and shot boundary detection algorithm in TV programs, to a large extent, optimizes the management of TV advertising and the manual broadcast of TV programs; moreover, it saves manpower and the broadcast cost of TV programs, which is a reform and innovation of traditional TV programs. In the future research, the boundary detection technology can be optimized to better play high-quality TV pictures.

TV program innovation and teaching under big data background in all media era

This exploration aims to better realize the cultural shaping and the value of digital media art under the background of Internet+. The security of digital media art information dissemination is discussed first. Then, the carrier image generation algorithm under the steganography process and deep learning is analyzed. After the improvement by edge computing (EC) and image steganography technology, the mean square error of carrier image generation algorithm is about 0.2 smaller than the three comparison algorithms, indicating that the optimized steganography technology is more stable. Meanwhile, the peak signal-to-noise ratio of the improved algorithm is between 0.06 and 0.2, and the structural similarity index measure is close to 1. Compared with traditional image steganography algorithm, multi-objective optimization based on genetic algorithm (MO-GA) algorithm improves the invisibility and security of steganography. Furthermore, the genetic algorithm is used to iteratively detect individuals with higher fitness of filtering residuals, to obtain the optimal solution of evolutionary multi-objective optimization problem. Finally, it is concluded that the MO-GA image steganography technology based on EC has advantages in the above three indicators, which improves the information security in the process of culture shaping and value realization of digital media.

Jiadi Yang

Papers

TV program innovation and teaching under big data background in all media era

Culture shaping and value realization of digital media art under Internet