Abstract: A method, computer program, and computer system is provided for video coding. Video data including one or more frames is received. A static background is estimated for each of the one or more frames based on a temporal average of the one or more frames. Pixels from among the one or more frames are identified as corresponding to the static background. Noise is removed in the static background based on the identified pixels.

Abstract: The embodiment of the present specification discloses an image synthesis method, apparatus and device for free-viewpoint.

Abstract: A method, computer program, and computer system for point cloud coding is provided. Data corresponding to a point cloud is received, and one or more geometric features are detected from among the data corresponding to the point cloud. A representation is determined for one or more of the detected geometric features, and the received data is encoded or decoded based on the determined representations whereby the point cloud is reconstructed based on the decoded data.

Abstract: In a method of point cloud geometry decoding in a point cloud decoder, chroma prediction residual information of a point in a set of points is received from a coded bitstream for a point cloud that includes the set of points. The chroma prediction residual information includes a Cb component and a Cr component. Further, a type of correlation between the Cb component and the Cr component of the chroma prediction residual information is determined by processing circuitry and from the coded bitstream for the point cloud. The chroma prediction residual information is decoded based on the type of the correlation between the Cb component and the Cr component of the chroma prediction residual information.

Abstract: A method, computer program, and computer system is provided for coding video data. Video data including a reference view and a current view is received. A co-located block in the reference view is identified for a current block in the current view. A predicted block vector is calculated based on an offset vector between the current block and the co-located block, and a disparity vector between the co-located block and the reference block in the reference view. The video data is encoded/decoded based on the calculated predicted block vector.

Abstract: A method of point cloud geometry decoding in a point cloud decoder is provided. In the method, first signaling information is received from a coded bitstream for a point cloud that includes a set of points in a three-dimensional (3D) space. The first signaling information indicates partition information of the point cloud. Second signaling information is determined based on the first signaling information indicating a first value. The second signaling information is indicative of a partition mode of the set of points in the 3D space. Further, the partition mode of the set of points in the 3D space is determined based on the second signaling information. The point cloud is reconstructed subsequently based on the partition mode.

Abstract: Aspects of the disclosure provide methods and apparatuses for video processing. In some examples, an apparatus for video processing includes processing circuitry. For example, processing circuitry determines a frame interval for a current block in a current frame within a sequence of frames. The frame interval indicates a group of frames in the sequence of frames with collocated blocks of the current block that satisfy an error metric requirement comparing to the current block. Further, the processing circuitry determines a replacement block based on the collocated blocks in the group of frames, and replaces the current block in the current frame with the replacement block.

Abstract: A method, a non-transitory computer readable medium, and a computer system is provided for encoding or decoding video data. The method may include: receiving an entropy coded bitstream comprising compressed video data including point cloud occupancy codes; generating one or more dequantized dimensions of a boundary box of a point cloud; based on determining that the compressed video data was predicted by using the attribute-based predictor, determining a predictor for decoding is the attribute-based predictor; based on determining that the compressed video data was predicted by using the attribute-based predictor, determining the predictor for decoding is the geometry-based predictor; and building an octree structure by using the determined predictor.

Abstract: A method and apparatus for coding information of a point cloud that includes obtaining the point cloud including a set of points in a three-dimensional space; determining whether a current node in the set of points is isolated; and coding the current node in isolation mode based on a determination that the current node is isolated and coding the current node in non-isolation mode, based on a determination that the current node is not isolated.

Abstract: Aspects of the disclosure provide methods and apparatuses for video processing. In some examples, an apparatus for video processing includes processing circuitry. For example, processing circuitry determines a frame interval for a current block in a current frame within a sequence of frames. The frame interval indicates a group of frames in the sequence of frames with collocated blocks of the current block that satisfy an error metric requirement comparing to the current block. Further, the processing circuitry determines a replacement block based on the collocated blocks in the group of frames, and replaces the current block in the current frame with the replacement block.

Abstract: The invention discloses A mechanical and electronic dual control water tap, including faucet group, control box and sensor, the sensor is arranged on the faucet group, and the faucet group is provided with a mixing valve; the mixing valve is internally arranged a cold water inlet, a hot water inlet, a first mixed water outlet and a second mixed water outlet are arranged; the second mixed water outlet is a normal water outlet channel; the control box includes a detection control unit, a first inflow runner, a second inflow runner and a mixed outlet channel; the first mixed water outlet is connected with the first inflow runner, and the second mixed water outlet is connected with the second inflow runner. The first inflow runner and the second inflow are respectively provided with inductor and solenoid valves.

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: The present application discloses a cross-media retrieval method based on deep semantic space, which includes a feature generation stage and a semantic space learning stage. In the feature generation stage, a CNN visual feature vector and an LSTM language description vector of an image are generated by simulating a perception process of a person for the image; and topic information about a text is explored by using an LDA topic model, thus extracting an LDA text topic vector. In the semantic space learning phase, a training set image is trained to obtain a four-layer Multi-Sensory Fusion Deep Neural Network, and a training set text is trained to obtain a three-layer text semantic network, respectively. Finally, a test image and a text are respectively mapped to an isomorphic semantic space by using two networks, so as to realize cross-media retrieval. The disclosed method can significantly improve the performance of cross-media retrieval.

Abstract: A method for coding information of a point cloud comprises obtaining the point cloud including a set of points in a three-dimensional space; partitioning the point cloud into a plurality of objects and generating occupancy information for each of the plurality of objects; and encoding the occupancy information by taking into account the distance between the plurality of objects.

Abstract: A method, a non-transitory computer readable medium, and a computer system is provided for encoding or decoding video data. The method may include: receiving an entropy coded bitstream comprising compressed video data including point cloud occupancy codes; generating one or more dequantized dimensions of a boundary box of a point cloud; based on determining that the node or node depth has attribute information, using the attribute information for the node or node depth; and based on determining that the node or node depth does not have attribute information, obtaining attribute information for the node or node depth by inheriting attribute information from a node or node depth that is at least one depth level in an octree of the point cloud above a depth level of the node or node depth in the octree.

Abstract: A method of decoding encoded information of a point cloud may be performed by at least one processor and comprises: obtaining an encoded bitstream, the encoded bitstream including encoded information of a point cloud including a set of points in a three-dimensional space; and determining a type of partitioning used to encode the information of the point cloud by at least one of parsing signals of at least three binary syntaxes or inferring at least one syntax of the at least three binary syntaxes.

Abstract: A method and apparatus for coding information of a point cloud may be performed by at least one processor and comprises: obtaining the point cloud including a set of points in a three-dimensional space; partitioning the point cloud into a tree structure comprising a plurality of nodes at different depths; encoding geometry information of the nodes; and encoding attribute information of the nodes before the entire point cloud is partitioned.

Abstract: A method and device for decoding a point cloud using octree partitioning and a predictive tree include obtaining the point cloud. A bounding box of the point cloud is determined. Octree nodes are generated by partitioning the bounding box using octree partitioning. The predictive tree is generated for points in at least one octree node of the octree nodes. A transform is applied to the predictive tree. The points in the at least one octree node are decoded using the predictive tree.

Abstract: The present application provides a method and a device of encoding and decoding based on free viewpoint, and relates to the technical field of video encoding. The method includes: generating a planar splicing image and splice information based on multiple single-viewpoint videos at a server side; generating a planar splicing video based on the planar splicing image; generating camera side information of the planar splicing video based on camera side information existing in the multiple single-viewpoint videos; and encoding the planar splicing video, the splice information and the camera side information of the planar splicing video to generate a planar splicing video bit stream, and decoding a planar splicing video bit stream to acquire a virtual viewpoint according to viewpoint information of a viewer at client side.