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21.发明授权公开(公告)号：US10015510B1
公开(公告)日：2018-07-03
申请号：US15501422
申请日：2016-05-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Pablo Navarrete Michelini , Lijie Zhang , Xiaoyu Li , Jianmin He
Abstract: The disclosure relates to an image compression system, an image decompression system, a training method and device, as well as a display device. In the image compression system, convolutional neural network modules are used to complete the update and prediction processes. As such, the weight of each filtering unit in the convolutional neural network module can be trained in order to provide the corresponding image compression system with a better compression rate, thereby reducing the difficulty in setting the filtering parameters of the image compression unit and the image decompression unit.
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公开(公告)号：US12177489B2
公开(公告)日：2024-12-24
申请号：US17615156
申请日：2021-01-20
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Lijie Zhang , Guannan Chen
IPC: H04N19/00 , H04N19/105 , H04N19/117 , H04N19/139 , H04N19/172 , H04N19/436 , H04N19/52 , H04N19/80
Abstract: The present disclosure relates to the field of image processing technology, and in particular, to image encoding, decoding methods and devices, an encoder-decoder. The method includes: acquiring a visual saliency heat map of an image of a current frame, and filtering, by using the visual saliency heat map of the image of the current frame, the image of the current frame to obtain a target image; acquiring, by using the target image and an input image of a next frame, a motion estimation vector and a target prediction image of the input image of the next frame; and encoding a difference image between the input image of the next frame and the target prediction image and the motion estimation vector.
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公开(公告)号：US12131584B2
公开(公告)日：2024-10-29
申请号：US17642781
申请日：2021-03-10
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Yanhong Wu , Guannan Chen , Pablo Navarrete Michelini , Lijie Zhang
IPC: G06V40/16
CPC classification number: G06V40/174 , G06V40/166 , G06V40/172
Abstract: An expression recognition method is described that includes acquiring a face image to be recognized, and inputting the face image into N different recognition models arranged in sequence for expression recognition and outputting an actual expression recognition result, the N different recognition models being configured to recognize different target expression types, wherein N is an integer greater than 1.
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公开(公告)号：US11800053B2
公开(公告)日：2023-10-24
申请号：US17278403
申请日：2020-05-29
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Yunhua Lu , Ran Duan , Guannan Chen , Lijie Zhang , Hanwen Liu
CPC classification number: H04N7/0137 , G06T3/40 , G06T7/246 , G06T7/269 , G06T7/50 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to the field of information display, and specifically to a method, device, computer readable storage medium, and electronic device for video frame interpolation. The method comprises: obtaining, based on two input frames, two initial optical flow maps corresponding to the two input frames; optimizing the initial optical flow maps to obtain target optical flow maps; obtaining an interpolation frame kernel, two depth maps and two context feature maps based on the two input frames; obtaining an output frame using a frame synthesis method based on the target optical flow maps, the depth maps, the context feature maps, and the interpolation frame kernel.
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公开(公告)号：US11689693B2
公开(公告)日：2023-06-27
申请号：US17265568
申请日：2020-04-30
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Yunhua Lu , Guannan Chen , Ran Duan , Lijie Zhang , Hanwen Liu
CPC classification number: H04N7/0135 , G06N20/00 , H04N7/0145
Abstract: A video frame interpolation method and device, and a computer-readable storage medium are described. The method includes: inputting at least two image frames into a video frame interpolation model to obtain at least one frame-interpolation image frame, training the initial model using a first loss to obtain a reference model, copying the reference model to obtain three reference models with shared parameters, selecting different target sample images according to a preset rules to train the first/second reference model to obtain a first/second frame-interpolation result; selecting third target sample images from the first/second frame-interpolation result to train the third reference model to obtain the frame-interpolation result, obtaining a total loss of the first training model based on the frame-interpolation result and the sample images, adjusting parameters of the first training model based on the total loss, and using a parameter model via a predetermined number of iterations as the video frame interpolation model.
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Patent Agency Ranking
公开(公告)号：US11537849B2
公开(公告)日：2022-12-27
申请号：US16626302
申请日：2019-07-22
Applicant: BOE Technology Group Co., Ltd.
Inventor： Dan Zhu , Lijie Zhang , Pablo Navarre Michelini , Hanwen Liu
Abstract: A computer-implemented method of training a convolutional neural network configured to morph content features of an input image with style features of a style image is provided. The computer-implemented method includes selecting a training style image; extracting style features of the training style image; selecting a training content image; extracting content features of the training content image; processing the training content image through the convolutional neural network to generate a training output image including the content features of the training content image morphed with the style features of the training style image; extracting content features and style features of the training output image; computing a total loss; and tuning the convolutional neural network based on the total loss including a content loss, a style loss, and a regularization loss.
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公开(公告)号：US20210326691A1
公开(公告)日：2021-10-21
申请号：US16492873
申请日：2019-03-22
Applicant: BOE Technology Group Co., Ltd.
Inventor： Hanwen Liu , Pablo Navarrete Michelini , Lijie Zhang , Dan Zhu
Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method using a convolutional neural network includes processing an input image through at least one channel of the convolutional neural network to generate an output image including content features of the input image morphed with style features of a reference style image. The at least one channel includes a down-sampling segment, a densely connected segment, and an up-sampling segment sequentially connected together. Processing the input image through the at least one channel of the convolutional neural network includes processing an input signal through the down-sampling segment to generate a down-sampling segment output; processing the down-sampling segment output through the densely connected segment to generate a densely connected segment output; and processing the densely connected segment output through the up-sampling segment to generate an up-sampling segment output. The input signal includes a component of the input image.
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28.发明授权公开(公告)号：US10825142B2
公开(公告)日：2020-11-03
申请号：US16062339
申请日：2017-11-29
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Lijie Zhang
Abstract: Proposed is a human face resolution re-establishing method based on machine learning, which retains overall structure information about a human face in the process of realizing image resolution improvement, and avoids the occurrence of a local distortion in a generated output image. The human face resolution re-establishing method includes: acquiring an input image, the input image having a first resolution; based on the input image and a standard gradient image library having a second resolution, determining image gradient information about the input image; fusing the image gradient information, and superposing the gradient information obtained through fusion onto the input image; and generating an output image, the output image having a third resolution, wherein the second resolution and the third resolution are both greater than the first resolution.
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公开(公告)号：US10565684B2
公开(公告)日：2020-02-18
申请号：US15526138
申请日：2016-02-03
Applicant: BOE Technology Group Co., Ltd.
Inventor： Lijie Zhang , Jianmin He , Pablo Navarrete Michelini , Xingxing Zhao
IPC: G06K9/62 , G06K9/32 , G06K9/40 , G06T3/40 , G06F16/51 , G06F16/56 , G06F16/583 , G06K9/00 , G06K9/46
Abstract: A super-resolution method, a super-resolution system, a user equipment and a server. The super-resolution method includes: training an image sample at a server; obtaining a server-side image database; updating a local image database in a user device by using the server-side image database; and displaying an input low-resolution image as a high-resolution image.
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公开(公告)号：US10311827B2
公开(公告)日：2019-06-04
申请号：US14761753
申请日：2014-09-30
Inventor： Shuhuan Yu , Xiao Zhang , Lijie Zhang , Xitong Ma , Peng Cheng
Abstract: There is provided an IC board and a display apparatus. Switching components (01; 02) are added between the internal interfaces (J1, J2 . . . Jn; j1, j2 . . . jn) corresponding to the backend data processing chips (U2; U3) and the frontend data processing chip (U1), or a switching component (02) is added between the internal interfaces (j1, j2 . . . jn) corresponding to the backend data processing chip (U2) and another backend data processing chip (U3). The switching components (01; 02) can ensure normal signal transmission between the backend data processing chips (U2; U3) and the frontend data processing chip (U1) or between the backend data processing chips (U2; U2) when no external test signal is input into the internal interfaces, i.e., when the IC board operates normally; and interrupt the signal transmission between the backend data processing chips (U2; U3) and the frontend data processing chip (U1) or between the backend data processing chips (U2; U3) when the internal interfaces j1, j2 . . . jn are input with an external test signal such that the impedance of the signal transmission path in the backend data processing chips (U2; U3) during the external testing remains consistent to avoid abnormal transmission of the external test signals and the signals during normal operation.
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