Abstract: The present disclosure provides a hybrid framework-based image bit-depth expansion method and device. The invention fuses a traditional de-banding algorithm and a depth network-based learning algorithm, and can remove unnatural effects in an image flat area whilst more realistically restoring numerical information of missing bits. The method comprises the extraction of image flat areas, local adaptive pixel value adjustment-based flat area bit-depth expansion and convolutional neural network-based non-flat area bit-depth expansion. The present invention uses a learning-based method to train an effective depth network to solve the problem of realistically restoring missing bits, whilst using a simple and robust local adaptive pixel value adjustment method in an flat area to effectively inhibit unnatural effects in the flat area such as banding, a ringing and flat noise, improving subjective visual quality of the flat area.

Abstract: A method, computer system, and computer-readable medium are provided for point cloud attribute coding by at least one processor. Data associated with a point cloud is received. The received data is transformed through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition. The point cloud is reconstructed based on the transformed data.

Abstract: A method of point cloud attribute coding is performed by at least one processor and includes receiving a point cloud data set comprising a bounding box. A determination is made as to whether the bounding box is symmetric or asymmetric. A quad-tree-binary-tree (QtBt) decomposition is performed on the point cloud data set based on the bounding box being asymmetric.

Abstract: The present application provides methods, systems, devices and computer-readable mediums for deblocking filter. A method of the present application comprises: determining a filtering boundary, and then determining a filter pixel group based on the filtering boundary; determining a filter strength of the filter pixel group, comprising: parsing separately a pixel value difference states of pixel points on both sides of the filtering boundary in the filter pixel group to obtain two one-sided flatness FL and FR; calculating a comprehensive flatness FS of the filter pixel group, wherein FS=FL+FR; calculating the filter strength according to FS; filtering calculation of pixel points included in the filter pixel group according to the filter strength.

Abstract: The present disclosure provides a SIM card management server, a binding device, a management method, a binding method and a binding system. By allocating a cloud SIM card and the network access information to an intelligent terminal of a user, so that the network access information of the original SIM card is replaced to acquire service from a carrier's network, and resource is conserved, the user is facilitated in flexibly replacing a cloud SIM card to access a network, and a system management of cloud SIM card is facilitated.

Abstract: An intra-frame and inter-frame combined prediction method for P frames or B frames. The method comprises: self-adaptively selecting by means of a rate-distortion optimization (RDO) decision whether to use the intra-frame and inter-frame combined prediction or not; using a method for weighting an intra prediction block and an inter prediction block in the intra-frame and inter-frame combined prediction to obtain a final prediction block; and obtaining the weighting coefficient of the intra prediction block and the inter prediction block according to prediction distortion statistics of the prediction method. Therefore, prediction precision can be improved, and coding and decoding efficiency of the prediction blocks are improved.

Abstract: Disclosed are a panoramic video asymmetrical mapping method and a corresponding inverse mapping method: by means of the mapping methods, mapping a spherical surface corresponding to a panoramic image or video A onto a two-dimensional image or video B; projecting the spherical surface onto an isosceles quadrangular pyramid with a square bottom plane, and further projecting the isosceles quadrangular pyramid onto a planar surface; using isometric projection on a main viewpoint region in the projection and using a relatively high sampling density to ensure that the video quality of the region of the main viewpoint is high, while using a relatively low sample density for non-main viewpoint regions so as to reduce bit rate.

Abstract: Disclosed is a method for detecting pedestrians in an image by using Gaussian penalty. Initial pedestrian boundary box is screened using a Gaussian penalty, to improve the pedestrian detection performance, especially sheltered pedestrians in an image. The method includes acquiring a training data set, a test data set and pedestrian labels of a pedestrian detection image; using the training data set for training to obtain a detection model by using a pedestrian detection method, and acquiring initial pedestrian boundary box and confidence degrees and coordinates thereof; performing Gaussian penalty on the confidence degrees of the pedestrian boundary box, to obtain confidence degree of the pedestrian boundary box after the penalty; and obtaining final pedestrian boundary boxes by screening the pedestrian boundary boxes. Thus, repeated boundary boxes of a single pedestrian are removed while reserving boundary boxes of sheltered pedestrians, thereby realizing the detection of the pedestrians in an image.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression. In some examples, an apparatus for point cloud compression includes processing circuitry. In some embodiments, the processing circuitry determines one or more original points in a point cloud that are associated with a reconstructed position. Positions of the one or more original points can be reconstructed, according to a geometry quantization, to the reconstructed position. The processing circuitry then determines an attribute value for the reconstructed position based on attribute information of the one or more original points, and encodes texture of the point cloud with the reconstructed position having the determined attribute value.

Abstract: An apparatus for point cloud decoding includes processing circuitry. The processing circuitry receives, from a coded bitstream for a point cloud, encoded occupancy codes for nodes in an octree structure for the point cloud. The nodes in the octree structure correspond to three dimensional (3D) partitions of a space of the point cloud. Sizes of the nodes are associated with sizes of the corresponding 3D partitions. Further, the processing circuitry decodes, from the encoded occupancy codes, occupancy codes for the nodes. At least a first occupancy code for a child node of a first node is decoded without waiting for a decoding of a second occupancy code for a second node having a same node size as the first node. Then, the processing circuitry reconstructs the octree structure based on the decoded occupancy codes for the nodes, and reconstructs the point cloud based on the octree structure.

Abstract: A bidirectional image-text retrieval method based on a multi-view joint embedding space includes: performing retrieval with reference to a semantic association relationship at a global level and a local level, obtaining the semantic association relationship at the global level and the local level in a frame-sentence view and a region-phrase view, and obtaining semantic association information in a global level subspace of frame and sentence in the frame-sentence view, obtaining semantic association information in a local level subspace of region and phrase in the region-phrase view, processing data by a dual-branch neural network in the two views to obtain an isomorphic feature and embedding the same in a common space, and using a constraint condition to reserve an original semantic relationship of the data during training, and merging the two semantic association relationships using multi-view merging and sorting to obtain a more accurate semantic similarity between data.

Abstract: Disclosed is a deep discriminative network for person re-identification in an image or a video. Concatenation are carried out on different input images on a color channel by constructing a deep discriminative network, and an obtained splicing result is defined as an original difference space of different images. The original difference space is sent into a convolutional network. The network outputs the similarity between two input images by learning difference information in the original difference space, thereby realizing person re-identification. The features of an individual image are not learnt, and concatenation are carried out on input images on a color channel at the beginning, and difference information is learnt on an original space of the images by using a designed network. By introducing an Inception module and embedding the same into a model, the learning ability of a network can be improved, and a better differentiation effect can be achieved.

Abstract: An image feature extraction method for person re-identification includes performing person re-identification by means of aligned local descriptor extraction and graded global feature extraction; performing the aligned local descriptor extraction by processing an original image by affine transformation and performing a summation pooling operation on image block features of same regions to obtain an aligned local descriptor; reserving spatial information between inner blocks of the image for the aligned local descriptor; and performing the graded global feature extraction by grading a positioned pedestrian region block and solving a corresponding feature mean value to obtain a global feature. The method can resolve the problem of feature misalignment caused by posture changes of pedestrian, etc., and eliminate the effect of unrelated backgrounds on re-recognition, thus improving the precision and robustness of person re-identification.

Abstract: Disclosed is a hierarchical division-based point cloud attribute compression method. For point cloud attribute information, a new hierarchical division based coding scheme is proposed, wherein a frame of point cloud is adaptively divided into a “stripe-macroblock-block” hierarchical structure according to the spatial position and color distribution of the point cloud, and stripes are coded independently from one another, increasing the coding efficiency, enhancing the fault tolerance of a system and improving the performance of point cloud attribute compression. The method comprises: (1) inputting a point cloud; (2) division of a k-dimension (KD) tree structure of the point cloud; (3) continuity analysis of point cloud attribute information; (4) stripe division of the point cloud; (5) division of macroblocks and coding blocks of the point cloud; and (6) intra-frame prediction, transformation, quantification and entropy coding based on a block structure.

Abstract: Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable medium for point cloud compression and decompression. In a method, syntax information of a bounding box of a point cloud is decoded from a coded bitstream. The syntax information indicates an octree partitioning structure for the bounding box of the point cloud. Whether the syntax information indicates that parallel decoding is to be performed on occupancy codes of nodes in a range of one or more partitioning depths in the octree partitioning structure is determined. The parallel decoding is performed on the occupancy codes of the nodes in response to the syntax information indicating that the parallel decoding is to be performed on the occupancy codes of the nodes in the range of the one or more partitioning depths in the octree partitioning structure. The bounding box is reconstructed based on the occupancy codes of the nodes.

Abstract: Disclosed is a shower apparatus, including a water flow body having a water output device with a water outlet mounted thereon. The water flow body includes a first water flow conduit and a second water flow conduit. The water output device includes a switch device having a third water flow conduit communicating with the first water flow conduit and the water outlet. The switch device also includes a fourth flow conduit communicating with the second water flow conduit. The switch device can switch between at least two water output effects. The water flow body includes a control device for controlling the water input through the first water flow conduit or the second water flow conduit. The invention can change the relative position of the switch device by the impact of the waterflow, thereby changing the water output effect of the water outlet to ameliorate the showering experience.

Abstract: The present application provides a filtering method for intra-frame and inter-frame prediction. For a pixel value obtained by prediction, whether to filter a prediction block is self-adaptively determined by means of an RDO decision; and a filter is established based on a generalized Gaussian distribution function, and different filters are used for prediction blocks of different sizes, and filter parameters are obtained by training the prediction blocks of each size. The method comprises performing RDO on each coding unit on a coding end, and firstly performing intra-frame prediction or inter-frame prediction on each coding unit; then filtering the prediction block using the filtering method of the present invention, and determining whether to use prediction block filtering by means of an RDO decision; on a decoding end, configuring different filtering coefficients for different prediction block sizes; and decoding according to a flag.

Abstract: Disclosed is a multi-angle adaptive intra-frame prediction-based point cloud attribute compression method. A novel block structure-based intra-frame prediction scheme is provided for point cloud attribute information, where six prediction modes are provided to reduce information redundancy among different coding blocks and improve the point cloud attribute compression performance. The method comprises: (1) inputting a point cloud; (2) performing point cloud attribute color space conversion; (3) dividing a point cloud by using a K-dimensional (KD) tree to obtain coding blocks; (4) performing block structure-based multi-angle adaptive intra-frame prediction; (5) performing intra-frame prediction mode decision; and (6) performing conversion, uniform quantization, and entropy encoding.

Abstract: The present application provides a filtering method for intra-frame and inter-frame prediction. For a pixel value obtained by prediction, whether to filter a prediction block is self-adaptively determined by means of an RDO decision; and a filter is established based on a generalized Gaussian distribution function, and different filters are used for prediction blocks of different sizes, and filter parameters are obtained by training the prediction blocks of each size. The method comprises performing RDO on each coding unit on a coding end, and firstly performing intra-frame prediction or inter-frame prediction on each coding unit; then filtering the prediction block using the filtering method of the present invention, and determining whether to use prediction block filtering by means of an RDO decision; on a decoding end, configuring different filtering coefficients for different prediction block sizes; and decoding according to a flag.

Abstract: Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable medium for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. The processing circuitry determines, for each point in a first point cloud, a corresponding point in a second point cloud. The first point cloud is a processed point cloud based on the second point cloud. The processing circuitry determines, for each point in the first point cloud, a corresponding error vector based on the corresponding point in the second point cloud. The processing circuitry determines, for each point in the first point cloud, a corresponding weight factor based on a position of the respective point in the first point cloud. The processing circuitry determines a quality metric for the first point cloud based on the error vectors and the weight factors.