Abstract: An image compression error detection method, comprising: acquiring a training image data set; extracting a first latent representation of the training image by an image compression model, wherein the first latent representation is a multi-channel latent representation for image compression; processing the first latent representation to obtain a stability measurement region for detecting an image compression error; and extracting a first latent representation of a test image, and comparing the first latent representation of the test image with the stability measurement region to obtain an image compression error detection result. This method can efficiently detect the error and corruption caused by the neural network-based image compression, and efficiently realizes stable continuous image compression, which is applicable to the actual image communication scene.

Abstract: The present disclosure provides a variable-bit-rate image compression method and system, an apparatus, a terminal, and a storage medium. The variable-bit-rate image compression method includes: obtaining an initial feature map from a to-be-encoded image; quantizing the initial feature map by a dead-zone quantizer; performing entropy encoding on the quantized feature map and hyper-prior information to obtain a compressed bit-stream; performing entropy decoding on the compressed bit-stream, and recovering quantized hyper-prior information and the quantized feature map; performing inverse quantization on the quantized feature map to obtain a reconstructed feature map; obtaining a reconstructed image from the reconstructed feature map; and adjusting quantization and inverse quantization parameters according to a target bit-rate or target distortion.

Abstract: The present disclosure provides a variable-bit-rate image compression method and system, an apparatus, a terminal, and a storage medium. The variable-bit-rate image compression method includes: obtaining an initial feature map from a to-be-encoded image; quantizing the initial feature map by a dead-zone quantizer; performing entropy encoding on the quantized feature map and hyper-prior information to obtain a compressed bit-stream; performing entropy decoding on the compressed bit-stream, and recovering quantized hyper-prior information and the quantized feature map; performing inverse quantization on the quantized feature map to obtain a reconstructed feature map; obtaining a reconstructed image from the reconstructed feature map; and adjusting quantization and inverse quantization parameters according to a target bit-rate or target distortion.

Abstract: Provided is a method for removing arsenic from copper smelting soot and comprehensive recovery of valuable metals. According to the method, a metal leaching synergist is prepared through thiol-ene click chemical reaction, which is capable of reacting more effectively with arsenic and metal impurities in the copper smelting soot due to its special chemical structure, thereby improving leaching efficiency; and the cage-like structure of the polysilsesquioxane provides excellent chemical stability, the removal rate of harmful substances in the copper smelting soot can be increased by using the synergist, environmental pollution is reduced, meanwhile, the recovery rate of metal resources is increased, and the requirements of green chemistry and sustainable development are met. The present disclosure realizes the centralized management of As and also realizes the step-by-step recovery of valuable metals such as Cu, Zn, Pb, Bi, and In.

Abstract: A three-dimensional point cloud upsampling method includes: dividing a three-dimensional point cloud into overlappable point cloud blocks which have a fixed number of points and are capable of covering all the points; extracting hierarchical features according to point coordinates in the point cloud blocks; achieving point set feature expansion of the extracted hierarchical features by using multi-scale heat kernel graph convolution; and reconstructing point coordinates in an upsampled three-dimensional point cloud from the expanded features. According to the present disclosure, detail information enhancement with different fine granularities can be performed on the three-dimensional point cloud which is sparse and nonuniformly distributed in the space, and at the same time, good stability is provided for overcoming potential noise disturbance and local deformation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to an encoded signal demodulation method, apparatus, and device. Some embodiments of the present disclosure are beneficial to improving demodulation performance.

Abstract: 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.

Abstract: A three-dimensional point cloud upsampling method includes: dividing a three-dimensional point cloud into overlappable point cloud blocks which have a fixed number of points and are capable of covering all the points; extracting hierarchical features according to point coordinates in the point cloud blocks; achieving point set feature expansion of the extracted hierarchical features by using multi-scale heat kernel graph convolution; and reconstructing point coordinates in an upsampled three-dimensional point cloud from the expanded features. According to the present disclosure, detail information enhancement with different fine granularities can be performed on the three-dimensional point cloud which is sparse and nonuniformly distributed in the space, and at the same time, good stability is provided for overcoming potential noise disturbance and local deformation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A smart home device control method, a medium, a terminal, and an apparatus. By approaching a target home device and obtaining device information thereof, and executing a device control program of a corresponding target home device according to the device information, the user operation is simplified, and the user experience of the home device is improved. The use of hardware multiplexing of a mobile terminal can enrich the function of the home device without significantly increasing the hardware cost of the home device, and reduce the design difficulty and complexity of the home device. The method includes: obtaining a distance between a target home device and a mobile terminal, and obtaining device information of the target home device when the distance is less than or equal to a preset distance.

Abstract: 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.

Abstract: 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.