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: 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.

Abstract: 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.

Abstract: 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.