IMAGE PROCESSING METHOD AND APPARATUS USING NEURAL NETWORK BASED DEBLOCKING FILTERING

    公开(公告)号:US20230044532A1

    公开(公告)日:2023-02-09

    申请号:US17880799

    申请日:2022-08-04

    Abstract: An image processing method and an image processing apparatus are provided to obtain input data for deblocking filtering based on deblocking filtering target pixels of at least one line perpendicular to a boundary line of blocks and encoding information about the deblocking filtering target pixels of at least one line, obtain a feature map of the input data by inputting the input data to a first neural network, obtain a deblocking filter coefficient by inputting the feature map to a second neural network, obtain a deblocking filter compensation value by inputting the feature map to a third neural network, obtain a deblocking filter strength by inputting the input data to a fourth neural network, obtain deblocking filtered pixels by performing deblocking filtering on the deblocking filtering target pixels of the at least one line using the deblocking filter coefficient, the deblocking filter compensation value, and the deblocking filter strength.

    IMAGE PROCESSING APPARATUS AND METHOD OF PROCESSING MULTI-FRAMES USING THE SAME

    公开(公告)号:US20220198628A1

    公开(公告)日:2022-06-23

    申请号:US17554827

    申请日:2021-12-17

    Abstract: An image processing apparatus, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: identify, in a previous frame, a prediction sample corresponding to a current sample of a current frame, generate a prediction frame for the current frame by changing a sample value of a collocated sample of the previous frame, wherein the collocated sample of the previous frame is collocated with the current sample, according to a sample value of the prediction sample, derive a weight by comparing a sample value of the current sample with the sample value of the prediction sample, apply the weight to a collocated sample of the prediction frame, wherein the collocated sample of the prediction frame is collocated with the current sample, to obtain a weighted prediction frame, and obtain a current output frame by processing the current frame and the weighted prediction frame through a neural network comprising a convolution layer.

    METHOD AND DEVICE FOR EVALUATING SUBJECTIVE QUALITY OF VIDEO

    公开(公告)号:US20210385502A1

    公开(公告)日:2021-12-09

    申请号:US17286743

    申请日:2019-09-26

    Abstract: Proposed are a method and apparatus for evaluating the quality of an image, the method including obtaining blocks each having a predetermined size by splitting a target image for evaluating a quality and a reference image that is to be compared with the target image, determining sensitivity information and quality assessment information of each of the blocks by inputting the blocks to a video quality assessment network, and determining a final image quality assessment score of the target image by combining the pieces of quality assessment information of the blocks with each other, based on the pieces of sensitivity information of the blocks.

    METHODS AND APPARATUSES FOR PERFORMING ARTIFICIAL INTELLIGENCE ENCODING AND ARTIFICIAL INTELLIGENCE DECODING ON IMAGE

    公开(公告)号:US20200219292A1

    公开(公告)日:2020-07-09

    申请号:US16821609

    申请日:2020-03-17

    Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to obtain the first image from the original image.

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