Abstract: The disclosure provides a method and an apparatus for determining a quality grade of video data, and relates to the field of data processing technologies, wherein the method includes: acquiring a plurality of initial EEG data; based on the plurality of initial EEG data, determining an initial EEG data set, wherein the initial EEG data set includes a first sub-data set and a second sub-data set, the first sub-data set is a data set built on the basis of emotional response electroencephalogram data, and the second sub-data set is a data set built on the basis of electroencephalogram emotion data; processing the first sub-data set and the second sub-data set by using a transfer learning algorithm to obtain a third sub-data set and a fourth sub-data set; and based on the third sub-data set and the fourth sub-data set, determining a quality evaluation grade of video data with degraded quality.

Abstract: The disclosure provides a method and an apparatus for determining a quality grade of video data, and relates to the field of data processing technologies, wherein the method includes: acquiring a plurality of initial EEG data; based on the plurality of initial EEG data, determining an initial EEG data set, wherein the initial EEG data set includes a first sub-data set and a second sub-data set, the first sub-data set is a data set built on the basis of emotional response electroencephalogram data, and the second sub-data set is a data set built on the basis of electroencephalogram emotion data; processing the first sub-data set and the second sub-data set by using a transfer learning algorithm to obtain a third sub-data set and a fourth sub-data set; and based on the third sub-data set and the fourth sub-data set, determining a quality evaluation grade of video data with degraded quality.

Abstract: A variable bit rate generative compression method based on adversarial learning is provided. According to the method, a variance of a feature map of an encoding-decoding fill convolutional network is quantized to train a single generative model to perform variable bit rate compression. The method includes the following implementation steps of: constructing training and testing data sets through an image acquisition device; constructing a generative compression network based on an auto-encoder structure; according to a rate-distortion error calculation unit, alternately training a generative network; according to a target compression rate, calculating a mask threshold; based on a feature map channel redundancy index, calculating a mask; and performing lossless compression and decoding on the mask and the feature map. According to the invention, only a single model is trained, but compression results with different bit rates can be generated, and on a limit compression rate below 0.1 bpp.