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公开(公告)号：US12046009B2
公开(公告)日：2024-07-23
申请号：US18590993
申请日：2024-02-29
发明人： Wenrui Dai , Xin Li , Shaohui Li , Chenglin Li , Junni Zou , Hongkai Xiong
摘要： A graph dictionary learning method for a 3D point cloud comprises: obtaining N point clouds to form training dataset; performing voxelization process on the point cloud data to obtain voxelized point cloud data of the training dataset; performing voxel block division on the point cloud data of the training dataset, selecting a plurality of voxel blocks as the training dataset, and constructing a graph dictionary learning model according to the training dataset; and performing iterative optimization on the graph dictionary learning objective function to obtain a graph dictionary for encoding and decoding a 3D point cloud signal. The present disclosure effectively uses the spatial correlation between point cloud signals to near-optimally remove the redundancy among point cloud signals.
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公开(公告)号：US11836954B2
公开(公告)日：2023-12-05
申请号：US18182401
申请日：2023-03-13
发明人： Wenrui Dai , Yangmei Shen , Chenglin Li , Junni Zou , Hongkai Xiong
摘要： In a 3D point cloud compression system based on multi-scale structured dictionary learning, a point cloud data partition module outputs a voxel set and a set of blocks of voxels of different scales. A geometric information encoding module outputs an encoded geometric information bit stream. A geometric information decoding module outputs decoded geometric information. An attribute signal encoding module outputs a sparse coding coefficient matrix and a learned multi-scale structured dictionary. An attribute signal compression module outputs a compressed attribute signal bit stream. An attribute signal decoding module outputs decoded attribute signals. A 3D point cloud reconstruction module completes reconstruction. The system is applicable to lossless geometric and lossy attribute compression of point cloud signals. Based on the natural hierarchical partitioning structure of point cloud signals, the system gradually improves the reconstruction quality of high-frequency details in the signals from coarse scale to fine scale, and achieves significant gains.
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公开(公告)号：US12106546B2
公开(公告)日：2024-10-01
申请号：US18623054
申请日：2024-04-01
发明人： Wenrui Dai , Yaoming Wang , Yuchen Liu , Chenglin Li , Junni Zou , Hongkai Xiong
IPC分类号： G06V10/00 , G06V10/764 , G06V10/776 , G06V10/82 , G06V20/70
CPC分类号： G06V10/765 , G06V10/776 , G06V10/82 , G06V20/70
摘要： The present disclosure provides an image classification method for maximizing mutual information, device, medium and system, the method including: acquiring a training image; maximizing the mutual information between the training image and a neural network architecture, and automatically determining the network architecture and parameter of the neural network; and processing image data to be classified using the obtained neural network to obtain an image classification result. According to the present disclosure, the network architecture and parameter of the neutral network are automatically designed and determined by maximizing the mutual information based on given image data without burdensome manual design and saving human and computational resource consumption. The present disclosure can automatically design and obtain a neural network-based image classification method in a very short time, and at the same time can achieve higher image classification accuracy.
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公开(公告)号：US10778982B2
公开(公告)日：2020-09-15
申请号：US16297717
申请日：2019-03-11
发明人： Hongkai Xiong , Chenglin Li
IPC分类号： H04N19/147 , H04N21/2343 , H04N21/2662 , H04N21/845 , H04N19/98 , H04N19/124 , H04N19/56 , H04N19/103
摘要： The disclosure provides a method and system for encoding bit rate control and version selection for a dynamic adaptive video streaming media. The method adopts a dynamic adaptive streaming media encoding technology to encode each original video into a plurality of versions with different bit rates at a server and determines video version subsets to be encoded by the original videos and specific encoding parameters of each video version by taking an encoding complexity-bit rate-distortion model for different original video contents, constraints on an encoding bit rate and a computing resource of the video server, network connection conditions of different users and a video-on-demand probability distribution into consideration, and finally, the video server outputs an optimal video version set through encoding, so as to maximize the overall quality of videos watched by users.
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公开(公告)号：US11995801B2
公开(公告)日：2024-05-28
申请号：US18472248
申请日：2023-09-22
发明人： Wenrui Dai , Ziyang Zheng , Chenglin Li , Junni Zou , Hongkai Xiong
CPC分类号： G06T5/60 , G06T5/00 , G06T5/70 , G06T2207/20084
摘要： An image processing method for sparse image reconstruction, image denoising, compressed sensing image reconstruction or image restoration, comprising: establishing a general linear optimization inverse problem under the 1-norm constraint of a sparse signal; establishing a differentiable deep network model based on convex combination to solve the problem on the basis of standard or learned iterative soft shrinkage thresholding algorithm; and introducing a deep neural network of arbitrary structure into the solving step to accelerate the solving step and reducing a number of iterations needed to reach a convergence. The present disclosure combines the traditional iterative optimization algorithm with the deep neural network of arbitrary structure to improve the image reconstruction performance and ensure fast convergence to meet the current needs of sparse image reconstruction.
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公开(公告)号：US11823432B2
公开(公告)日：2023-11-21
申请号：US18164610
申请日：2023-02-05
发明人： Chenglin Li , Haoran Lv , Qin Yang , Junni Zou , Wenrui Dai , Hongkai Xiong
IPC分类号： G06V10/46 , G06T5/00 , G06T5/20 , H04N23/698 , G06V10/44 , G06V10/82 , G06V10/426
CPC分类号： G06V10/462 , G06T5/002 , G06T5/20 , G06V10/426 , G06V10/44 , G06V10/82 , H04N23/698
摘要： The present disclosure provides a saliency prediction method and system for a 360-degree image based on a graph convolutional neural network. The method includes: firstly, constructing a spherical graph signal of an image of an equidistant rectangular projection format by using a geodesic icosahedron composition method; then inputting the spherical graph signal into the proposed graph convolutional neural network for feature extraction and generation of a spherical saliency graph signal; and then reconstructing the spherical saliency graph signal into a saliency map of an equidistant rectangular projection format by using a proposed spherical crown based interpolation algorithm. The present disclosure further proposes a KL divergence loss function with sparse consistency. The method can achieve excellent saliency prediction performance subjectively and objectively, and is superior to an existing method in computational complexity.
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