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    IMAGE PROCESSING APPARATUSES INCLUDING CNN-BASED IN-LOOP FILTER
    21.
    发明申请
    IMAGE PROCESSING APPARATUSES INCLUDING CNN-BASED IN-LOOP FILTER 有权

    公开(公告)号：US20230134212A1

    公开(公告)日：2023-05-04

    申请号：US18088615

    申请日：2022-12-26

    Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

    Inventor： Mun Churl KIM

    IPC: H04N19/117 ,  H04N19/82 ,  G06F17/15 ,  H04N19/176 ,  G06N3/04 ,  H04N19/124

    Abstract: Disclosed according to one exemplary embodiment includes not limited to: a filtering unit configured to generate filtering information by filtering a residual image corresponding to a difference between an original image and a prediction image; an inverse filtering unit configured to generate inverse filtering information by inversely filtering the filtering information; an estimator configured to generate the prediction image based on the original image and reconstruction information; a CNN-based in-loop filter configured to receive the inverse filtering information and the prediction image and to output the reconstruction information; and an encoder configured to perform encoding based on the filtering information and information of the prediction image, and wherein the CNN-based in-loop filter is trained for each of the plurality of artefact sections according to an artefact value or for each of the plurality of quantization parameter sections according to a quantization parameter.

    VIDEO PROCESSING METHOD AND APPARATUS
    22.
    发明申请
    VIDEO PROCESSING METHOD AND APPARATUS 有权

    公开(公告)号：US20220366538A1

    公开(公告)日：2022-11-17

    申请号：US17623270

    申请日：2020-07-03

    Applicant: Korea Advanced Institute of Science and Technology

    Inventor： Mun Churl KIM ,  Se Hwan KI

    IPC: G06T5/00 ,  G06T7/11 ,  G06T3/40

    Abstract: Disclosed are a video processing method and a device therefor. The video processing method may include receiving a video comprising a plurality of temporal portions, receiving a first model parameter corresponding to a first neural network to process the video entirely, receiving residues between the first model parameter and a plurality of second model parameters corresponding to a plurality of second neural networks to process the plurality of temporal portions, and performing at least one of super-resolution, reverse or inverse tone mapping, tone mapping, frame interpolation, motion deblurring, denoising, and compression artifact removal on the video based on the residues.

    METHOD AND APPARATUS FOR INVERSE TONE MAPPING
    23.
    发明申请
    METHOD AND APPARATUS FOR INVERSE TONE MAPPING 有权

    公开(公告)号：US20220012855A1

    公开(公告)日：2022-01-13

    申请号：US17482433

    申请日：2021-09-23

    Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

    Inventor： Mun Churl KIM ,  Soo Ye KIM ,  Dae Eun KIM

    IPC: G06T5/00 ,  G06F17/11 ,  G06N3/02 ,  G06N3/08 ,  G06T5/50

    Abstract: In this invention, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this invention first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.

    IMAGE PIPELINE PROCESSING METHOD AND DEVICE
    24.
    发明申请
    IMAGE PIPELINE PROCESSING METHOD AND DEVICE 有权

    公开(公告)号：US20210082087A1

    公开(公告)日：2021-03-18

    申请号：US16960907

    申请日：2018-12-12

    Applicant: Korea Advanced Institute of Science and Technology

    Inventor： Mun Churl KIM ,  Yong Woo KIM ,  Jae Seok CHOI

    IPC: G06T3/40 ,  H04N7/01 ,  G06T1/20 ,  H04N19/124

    Abstract: Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; at least one first line buffer for outputting the image into a line-wise image line; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the output from the first line buffer; and a feature map processor for storing the output from the first convolution operator in units of at least one line, and processing so as to output the feature map stored in units of at least one line into a two-dimensional form, wherein at least one convolution operation operates in the form of a pipeline.

    ENCODING AND DECODING APPARATUSES INCLUDING CNN-BASED IN-LOOP FILTER
    29.
    发明申请
    ENCODING AND DECODING APPARATUSES INCLUDING CNN-BASED IN-LOOP FILTER 有权

    公开(公告)号：US20210344916A1

    公开(公告)日：2021-11-04

    申请号：US17376162

    申请日：2021-07-15

    Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

    Inventor： Mun Churl KIM

    IPC: H04N19/117 ,  H04N19/82 ,  G06F17/15 ,  H04N19/176 ,  G06N3/04 ,  H04N19/124

    Abstract: Disclosed according to one exemplary embodiment includes not limited to: a filtering unit configured to generate filtering information by filtering a residual image corresponding to a difference between an original image and a prediction image; an inverse filtering unit configured to generate inverse filtering information by inversely filtering the filtering information; an estimator configured to generate the prediction image based on the original image and reconstruction information; a CNN-based in-loop filter configured to receive the inverse filtering information and the prediction image and to output the reconstruction information; and an encoder configured to perform encoding based on the filtering information and information of the prediction image, and wherein the CNN-based in-loop filter is trained for each of the plurality of artefact sections according to an artefact value or for each of the plurality of quantization parameter sections according to a quantization parameter.

    METHOD AND APPARATUS FOR INVERSE TONE MAPPING
    30.
    发明申请
    METHOD AND APPARATUS FOR INVERSE TONE MAPPING 有权

    公开(公告)号：US20210166360A1

    公开(公告)日：2021-06-03

    申请号：US16769576

    申请日：2017-12-06

    Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

    Inventor： Mun Churl KIM ,  Soo Ye KIM ,  Dae Eun KIM

    IPC: G06T5/00 ,  G06T5/50

    Abstract: Inverse tone mapping (ITM) aims at generating a single high dynamic range (HDR) image from a low dynamic range (LDR) image. While ITM was frequently used for graphics rendering in the HDR space, the advent of HDR consumer displays (e.g., HDR TV) and the consequent need for HDR multimedia contents open up new horizons for the consumption of ultra-high quality video contents. However, due to the lack of HDR-filmed contents, the legacy LDR videos must be up-converted for viewing on these HDR displays. Unfortunately, the previous ITM methods are not appropriate for HDR consumer displays, and their inverse-tone-mapped results are not visually pleasing with noise amplification or lack of details. In this paper, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this paper first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.

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