Video comparison method and apparatus, computer device, and storage medium

A video comparison apparatus, comprising:

• a memory and a processor, the memory storing computer-readable instructions, the processor being configured, when executing the computer-readable instructions, to:
• obtain a first video and a second video;
• obtain a first image sequence from the first video, and obtain a second image sequence from the second video;
• extract a first definition feature vector of the first image sequence by using a first feature extraction mechanism of a video comparison model, comprising:
• mapping first images in the first image sequence from a pixel space to a target embedding space by inputting the first image sequence into a convolutional neural network (CNN) in a first feature extraction layer of the first feature extraction mechanism, to obtain a first image feature vector of the first image sequence;
• performing, based on a quantity of cells and a quantity of neurons of a long short-term memory (LSTM) network in a second feature extraction layer of the first feature extraction mechanism, a deformation operation on the first image feature vector outputted by the CNN, to obtain a reshaped first image feature vector;
• and
• analyzing the first image feature vector based on a time sequence relationship among the first images corresponding to the first image feature vector by inputting the reshaped first image feature vector to the LSTM network in the second feature extraction layer of the first feature extraction mechanism, to obtain the first definition feature vector corresponding to the first image sequence;
• extract a second definition feature vector of the second image sequence by using a second feature extraction mechanism of the video comparison model, the first feature extraction mechanism being the same as the second feature extraction mechanism;
• and
• determine a definition difference between the first video and the second video based on the first definition feature vector and the second definition feature vector by using a definition difference analysis mechanism of the video comparison model, the definition difference indicating whether a video clarity of the first video is higher or lower than a video clarity of the second video and being determined by:
• performing a subtraction operation on the first definition feature vector and the second definition feature vector to obtain a subtraction result, and directly using the subtraction result as vector difference between the first definition feature vector and the second definition feature vector;
• and
• using at least one fully connected layer on the vector difference between the first definition feature vector and the second definition feature vector to obtain the definition difference.

A non-transitory storage medium storing computer-readable instructions, the computer-readable instructions, when executed by one or more processors, causing the one or more processors to perform:

• obtaining a first video and a second video;
• obtaining a first image sequence from the first video, and obtaining a second image sequence from the second video;
• extracting a first definition feature vector of the first image sequence by using a first feature extraction mechanism of a video comparison model, comprising:
• mapping first images in the first image sequence from a pixel space to a target embedding space by inputting the first image sequence into a convolutional neural network (CNN) in a first feature extraction layer of the first feature extraction mechanism, to obtain a first image feature vector of the first image sequence;
• performing, based on a quantity of cells and a quantity of neurons of a long short- term memory (LSTM) network in a second feature extraction layer of the first feature extraction mechanism, a deformation operation on the first image feature vector outputted by the CNN, to obtain a reshaped first image feature vector;
• and
• analyzing the first image feature vector based on a time sequence relationship among the first images corresponding to the first image feature vector by inputting the reshaped first image feature vector to the LSTM network in the second feature extraction layer of the first feature extraction mechanism, to obtain the first definition feature vector corresponding to the first image sequence;
• extracting a second definition feature vector of the second image sequence by using a second feature extraction mechanism of the video comparison model, the first feature extraction mechanism being the same as the second feature extraction mechanism;
• and
• determining a definition difference between the first video and the second video based on the first definition feature vector and the second definition feature vector by using a definition difference analysis mechanism of the video comparison model, the definition difference indicating whether a video clarity of the first video is higher or lower than a video clarity of the second video and being determined by:
• performing a subtraction operation on the first definition feature vector and the second definition feature vector to obtain a subtraction result, and directly using the subtraction result as vector difference between the first definition feature vector and the second definition feature vector;
• and
• using at least one fully connected layer on the vector difference between the first definition feature vector and the second definition feature vector to obtain the definition difference.