Traditional rate optimization algorithms typically use peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) as optimization objectives to improve video quality while reducing bitrate. However, in machine v...Traditional rate optimization algorithms typically use peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) as optimization objectives to improve video quality while reducing bitrate. However, in machine vision applications, machines are usually not concerned with video quality but rather the semantic information conveyed by the video. In this paper, focusing on the application scenario of connected vehicles, a bitrate control algorithm for the 5th generation mobile communication technology(5G) roadside perception cameras in the context of connected vehicles is studied. Specifically, a semantic-driven rate optimization algorithm is proposed. This algorithm introduces a video semantic task model and adaptively allocates bitrate by dividing video frames into multiple independent encoding regions based on video content features. It effectively controls the bitrate while maximizing the semantic task. Experimental results show that compared with the high efficiency video coding(HEVC) test model HM16.23 encoder using the bidirectional mainstream largest coding unit(LCU)-layer model bitrate control algorithm, the semantic-driven bitrate optimization algorithm(SDBOA) proposed in this article saves an average of 10.1% of the bitrate. Compared with the HMLCU1 algorithm, the target detection accuracy is improved by 3.54% on average. Compared with the HMLCU2 algorithm, the target detection accuracy is improved by 7.54% on average. SDBOA is more in line with the current video processing scenarios in mainstream semantic analysis tasks.展开更多
基金supported by the Science Foundation of the Fujian Province(2022J01551)。
文摘Traditional rate optimization algorithms typically use peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) as optimization objectives to improve video quality while reducing bitrate. However, in machine vision applications, machines are usually not concerned with video quality but rather the semantic information conveyed by the video. In this paper, focusing on the application scenario of connected vehicles, a bitrate control algorithm for the 5th generation mobile communication technology(5G) roadside perception cameras in the context of connected vehicles is studied. Specifically, a semantic-driven rate optimization algorithm is proposed. This algorithm introduces a video semantic task model and adaptively allocates bitrate by dividing video frames into multiple independent encoding regions based on video content features. It effectively controls the bitrate while maximizing the semantic task. Experimental results show that compared with the high efficiency video coding(HEVC) test model HM16.23 encoder using the bidirectional mainstream largest coding unit(LCU)-layer model bitrate control algorithm, the semantic-driven bitrate optimization algorithm(SDBOA) proposed in this article saves an average of 10.1% of the bitrate. Compared with the HMLCU1 algorithm, the target detection accuracy is improved by 3.54% on average. Compared with the HMLCU2 algorithm, the target detection accuracy is improved by 7.54% on average. SDBOA is more in line with the current video processing scenarios in mainstream semantic analysis tasks.
