Detection Method Of Video Quality Abnormal State

Promote harmonious living in today's society,security systems to ensure the stable operation is essential.As an important part of video surveillance security systems,to ensure property safety,personal safety,and played an indispensable role.With the increase of surveillance cameras,security staff to ensure the stability of the monitoring system,will greatly increase the workload of staff,some of the potential problems also will show up on the video quality of the abnormality detection is one of them.At present,the majority of video quality abnormality detection system is basically rely on manual testing methods,but close to the manual detection methods to detect,heavy workload,the method cumbersome,time-consuming and labor-intensive,is not conducive to reduce costs,and camera the emergence of abnormal image transmission quality is random,manual inspection does not guarantee the exact moment the camera detects the working state,which will inevitably affect the monitoring results.Thus,real-time automatic detection of the abnormal state of the video camera,the image quality is necessary.Video surveillance equipment in image acquisition,compression,transmission and storage process,may become distorted and degraded phenomenon can cause serious and even monitor failure.Common abnormal state include:color cast,brightness anomalies,flutter,signal is missing,noise,interference fringes,shelter,and other sudden changes in the scene.The common type of image abnormal state,the author of this paper,first of all,analyses the causes and in the structure of image rendering features;Then describes some common detection methods to analyze the advantages and disadvantages of these detection methods;and finally,according to the characteristics of the abnormal state and previous detection methods,proposed the corresponding detection algorithm.In addition,this paper focuses on the abnormal image noise,local foreign body blocking the abnormal detailed analysis,and for the static background and dynamic backgrounds,different detection methods proposed by experimental verification of the feasibility of the algorithm.Experimental results show that the algorithm proposed accurate test results,and high availability,suitable for use in real-time monitoring system.