A method for video encoding flow rate control at the monitoring video sending end

Abstract

The invention discloses a method for controlling the video encoding flow rate of a monitoring video sending end. The main steps include: (1) collecting a real-time available bandwidth data set of a video sending scene network; (2) constructing a simulation training environment for a monitoring video sending end by using real bandwidth data , the training environment determines the highest available bandwidth for monitoring video transmission in real time according to the real bandwidth data, as the video transmission rate, and receives the code rate selected by the deep reinforcement learning model to adjust the code rate of the encoder; (3) Construct a trust-region-based continuous The action outputs the deep reinforcement learning model, and uses the simulation environment to train the model; (4) put the trained model into the surveillance video and integrate it into the real environment for interaction, and conduct online training optimization; (5) optimize the deep learning model It is integrated into the monitoring video sending end to make the encoding rate decision of the encoder at the sending end. The invention solves the problem of controlling the encoding flow rate at the monitoring video sending end by using deep reinforcement learning.

Description

technical field [0001] The invention relates to the field of real-time video transmission, in particular to a method for controlling the video coding flow rate of a monitoring video monitoring terminal. Background technique [0002] Surveillance video usually has high requirements in terms of real-time, fluency, and video picture quality. However, in the actual monitoring environment, from the monitoring video collection end to the receiving end taking the monitoring room as an example, the video transmission often passes through a relatively complex network environment, which will lead to bandwidth limitations and delay fluctuations. The situation occurs, thereby affecting the real-time performance, fluency and clarity of the monitoring video playback end (receiving end). In order to ensure the transmission effect of surveillance video and improve the viewing experience of surveillance video, it is necessary to optimize all links in the surveillance video transmission proc...

AI Technical Summary

Problems solved by technology

However, in the actual environment, since the change of the actual available bandwidth is usually irregular, it is quite difficult to estimate the actual available bandwidth at the next moment, so it can only be observed by observing some measurable characteristics during the video transmission process Parameters to make a rough estimate of the current transmission environment, and select the code rate of the encoder at the next moment according to the observed characteristic parameters

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