Abstract: Disclosed is an image processing device. The image processing device includes an input unit, and a processor which retargets an image frame input through the input unit and acquires an output image frame, wherein the processor identifies an area, in which pixel values are to be padded, on the basis of the input image frame and information about the output image frame, identifies a first pixel block on the basis of a target pixel area included in the identified area, identifies a second pixel block on the basis of pixel values included in the first pixel block, and pads the target pixel area on the basis of pixel values included in the second pixel block, so as to able to acquire the output image frame.

Abstract: An image processing apparatus is provided. The image processing apparatus includes a memory configured to store at least one instruction; and a processor configured to read the at least one instruction and configured to, according to the at least one instruction: apply a learning network model to an input image frame and acquire information on an area of interest; and acquire an output image frame by retargeting the input image frame based on the acquired information on the area of interest. The learning network model is a model that is trained to acquire the information on the area of interest in the input image frame.

Abstract: An image processing apparatus is provided. The image processing apparatus includes a memory configured to store at least one instruction; and a processor configured to read the at least one instruction and configured to, according to the at least one instruction: apply a learning network model to an input image frame and acquire information on an area of interest; and acquire an output image frame by retargeting the input image frame based on the acquired information on the area of interest. The learning network model is a model that is trained to acquire the information on the area of interest in the input image frame.