Image processing apparatus and method thereof

    公开(公告)号:US11138437B2

    公开(公告)日:2021-10-05

    申请号:US16685553

    申请日:2019-11-15

    Abstract: Provided are an image processing apparatus and method. The image processing apparatus includes a decoder configured to decode image frames of an image; an image quality controller configured to obtain a genre recognition confidence of a previous image frame and a genre recognition confidence of a current image frame, and identify image quality control factor value, based on the genre recognition confidence of the previous image frame and the genre recognition confidence of the current image frame; and an image quality processor configured to image-quality process at least one of the decoded image frames by using the image quality control factor value, and output the processed at least one of the decoded image frames.

    Electronic device for performing video quality assessment, and operation method of the electronic device

    公开(公告)号:US12266165B2

    公开(公告)日:2025-04-01

    申请号:US17824587

    申请日:2022-05-25

    Abstract: An electronic device is provided. The electronic device includes a memory storing one or more instructions, and a processor configured to execute the one or more instruction stored in the memory. The processor is configured to execute the one or more instructions to obtain a subjective assessment score for each of a plurality of sub-regions included in an input frame, the subjective assessment score being a Mean Opinion Score (MOS); obtain a location weight for each of the plurality of sub-regions, the location weight indicating characteristics according to a location of a display; obtain a weighted assessment score for each of the plurality of sub-regions, based on the subjective assessment score for each of the plurality of sub-regions and the location weight for each of the plurality of sub-regions; and obtain a final quality score for the entire video frame, based on the weighted assessment score for each of the plurality of sub-regions.

    AI downscaling apparatus and operating method thereof, and AI upscaling apparatus and operating method thereof

    公开(公告)号:US12254591B2

    公开(公告)日:2025-03-18

    申请号:US18638310

    申请日:2024-04-17

    Abstract: An artificial intelligence (AI) upscaling apparatus for upscaling a low-resolution image to a high-resolution image includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to: obtain a second image corresponding to a first image, which is downscaled from an original image by an AI downscaling apparatus by using a first deep neural network (DNN); and obtain a third image by upscaling the second image by using a second DNN corresponding to the first DNN, and wherein the second DNN is trained to minimize a difference between a first restored image, which results from applying no pixel movement to an original training image, and second restored images, which result from downscaling, upscaling, and subsequently retranslating one or more translation images obtained by applying pixel movement to the original training image.

    AI downscaling apparatus and operating method thereof, and AI upscaling apparatus and operating method thereof

    公开(公告)号:US11989852B2

    公开(公告)日:2024-05-21

    申请号:US17312276

    申请日:2021-01-11

    CPC classification number: G06T3/4046 G06T3/4053

    Abstract: An artificial intelligence (AI) upscaling apparatus for upscaling a low-resolution image to a high-resolution image includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to: obtain a second image corresponding to a first image, which is downscaled from an original image by an AI downscaling apparatus by using a first deep neural network (DNN); and obtain a third image by upscaling the second image by using a second DNN corresponding to the first DNN, and wherein the second DNN is trained to minimize a difference between a first restored image, which results from applying no pixel movement to an original training image, and second restored images, which result from downscaling, upscaling, and subsequently retranslating one or more translation images obtained by applying pixel movement to the original training image.

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