A super-resolution processing method of a video, the method comprising: sequentially inputting a plurality of frames included in the video to a recurrent neural network, RNN, for super-resolution processing and a convolutional neural network, CNN, for super-resolution processing; the method characterized by: generating, using the RNN, a prediction of a hidden state of a current frame based on a hidden state of a previous frame, and updating the current frame with the prediction of the hidden state; sequentially inputting a plurality of frames being sequentially output from the RNN and containing the hidden state prediction, into the CNN, wherein the CNN outputs a feature map for each frame comprising new pixel information and the hidden state prediction; and upscaling a resolution of a plurality of frames, by performing deconvolution and convolution of the feature map for each frame that comprises the prediction of the hidden state and the new pixel information that is output from the CNN; wherein the RNN is configured to: predict a scene change of the video by using information on the hidden state of the previous frame; based on the scene change being predicted, change information on the hidden state of the previous frame to a zero value; and update information on the hidden state of the current frame based on the information on the hidden state of the previous frame changed to the zero value. sequentially inputting a plurality of frames included in the video to a recurrent neural network, RNN, for super-resolution processing and a convolutional neural network, CNN, for super-resolution processing; the method characterized by: generating, using the RNN, a prediction of a hidden state of a current frame based on a hidden state of a previous frame, and updating the current frame with the prediction of the hidden state; sequentially inputting a plurality of frames being sequentially output from the RNN and containing the hidden state prediction, into the CNN, wherein the CNN outputs a feature map for each frame comprising new pixel information and the hidden state prediction; and upscaling a resolution of a plurality of frames, by performing deconvolution and convolution of the feature map for each frame that comprises the prediction of the hidden state and the new pixel information that is output from the CNN; generating, using the RNN, a prediction of a hidden state of a current frame based on a hidden state of a previous frame, and updating the current frame with the prediction of the hidden state; sequentially inputting a plurality of frames being sequentially output from the RNN and containing the hidden state prediction, into the CNN, wherein the CNN outputs a feature map for each frame comprising new pixel information and the hidden state prediction; and upscaling a resolution of a plurality of frames, by performing deconvolution and convolution of the feature map for each frame that comprises the prediction of the hidden state and the new pixel information that is output from the CNN; wherein the RNN is configured to: predict a scene change of the video by using information on the hidden state of the previous frame; based on the scene change being predicted, change information on the hidden state of the previous frame to a zero value; and update information on the hidden state of the current frame based on the information on the hidden state of the previous frame changed to the zero value. predict a scene change of the video by using information on the hidden state of the previous frame; based on the scene change being predicted, change information on the hidden state of the previous frame to a zero value; and update information on the hidden state of the current frame based on the information on the hidden state of the previous frame changed to the zero value.