Video image quality anomaly classification method based on GoogleNet

The invention discloses a video image quality anomaly classification method based on GoogleNet, and relates to the technical field of video image quality processing, and the method comprises the following steps: obtaining original data information in advance, and carrying out the data preprocessing which comprises the steps: marking the data information, carrying out the data augmentation, splitting a data set, and generating a tfrecore file; establishing a neural network model and performing training, wherein the neural network model comprises a pre-training weight of a calibration Image Netimage data set, a convolution base of a calibration model and a dense connection classifier; and taking the trained neural network model as a video image quality anomaly classification model and outputting a result. The method is simple to implement, high in classification accuracy, high in speed, capable of detecting image quality anomalies in real time and classifying the anomalies, convenient to operate and convenient to expand.