Adaptive video compression coding optimization method combined with genetic algorithm

Traditional video compression coding methods, such as H.26x series and MPEG series standards, have some problems such as low coding efficiency and poor adaptability, and it is difficult to achieve the best balance between coding efficiency and video quality. As a heuristic search algorithm, (GA) can effectively solve complex optimization problems by simulating the genetic and mutation mechanism in the process of biological evolution. In this paper, the key parameters of video compression and coding are optimized by GA, and the parameters and strategies of GA are adaptively adjusted according to the characteristics of video content and coding requirements. The experimental results show that this method is superior to x264 and x265 standard configurations in video coding performance. In the Basketball video sequence test, the highest SSIM value is 0.941, the PSNR value is 38.3 dB, and the lowest bit rate is 6.8 Mbps. Although the coding time is slightly longer, it still meets the real-time requirements by limiting the maximum number of iterations. This method also has good convergence characteristics and adaptive parameter adjustment ability, and can dynamically adjust parameters such as population size, crossover probability and mutation intensity according to video content, thus effectively improving coding performance.