Abstract: A method, computer program, and computer system is provided for point cloud coding. Data corresponding to a point cloud is received. Hash elements corresponding to attribute values associated with the received data is reconstructed. A size of a hash table may be decreased based on deleting one or more of the hash elements corresponding to non-border regions associated with the attribute values. The data corresponding to the point cloud is decoded based on the reconstructed hash elements.

Abstract: The present disclosure discloses an inverse tone mapping method, system, device and computer readable medium The method of embodiment of the present application comprises: decomposing the original image into an illumination component and a reflection component, wherein the illumination component represents a global illumination condition of the image, the reflection component representing a color and texture detail of the image; recovering the illumination component to obtain a result of illumination component recovery; recovering the reflection component to obtain a result of reflection component recovery; combining the result of the illumination component recovery and the result of the reflection component recovery to obtain a recovery result image. Compared with the prior art, the inverse tone mapping method according to the embodiment of the present invention can greatly improve the effect of the image recovery.

Abstract: A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; obtaining from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud; reconstructing the first prediction residual; determining a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter; obtaining a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual; reconstructing the second prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.

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: The present application provides methods, systems, devices and computer-readable mediums for encoding and decoding transform. A method of the present application comprises: performing transform-encoding on a prediction residual block separately using a plurality of transform matrices, to obtain transformed residual blocks; based on the transformed residual block, determining, by a rate-distortion optimization decision, a transform matrix or a combination of transform matrices matching the residual characteristics of the prediction residual block from the plurality of transform matrices, and determining a transformed residual block to be outputted in a bitstream. Compared with the prior art, the method of the embodiments of the present invention performs a residual transform using transform matrices that are more closely matched with the residual characteristic, thereby improving the expression of the residual signal and improving the coding efficiency of the residual block.

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 point cloud is decoded from a coded bitstream. 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 an octree partitioning structure of the point cloud is determined. The parallel decoding is performed on the occupancy codes of the nodes. The point cloud is reconstructed based on the occupancy codes of the nodes.

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: 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 back-propagation significance detection method based on depth map mining, comprising: for an input image Io, at a preprocessing phase, obtaining a depth image Id and an image Cb with four background corners removed of the image Io; at a first processing phase, carrying out positioning detection on a significant region of the image by means of the obtained image Cb with four background corners removed and the obtained depth image Id to obtain the preliminary detection result S1 of a significant object in the image; then carrying out depth mining on a plurality of processing phases of the depth image Id to obtain corresponding significance detection results; and then optimizing the significance detection result mined in each processing phase by means of a back-propagation mechanism to obtain a final significance detection result map. The method can improve the detection accuracy of the significance object.

Abstract: A method, computer program, and computer system is provided for processing point cloud data. Quantized point cloud data including a plurality of voxels is received. An occupancy map is generated for the quantized point cloud corresponding to lost voxels during quantization from among the plurality of voxels. A point cloud is reconstructed from the quantized point cloud data based on populating the lost voxels.

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: Disclosed in the present invention is a point cloud attribution compression method based on deleting 0 elements in a quantisation matrix, including optimizing a traversal sequence for a quantisation matrix and deleting the 0 elements at the end of the data stream. The present invention may use seven types of traversal sequences at the encoding end of the point cloud attribute compression, such that the distribution of the 0 elements in the data stream may be more concentrated at the end thereof. The 0 elements at the end of the data stream may be deleted, removing redundant information and reducing the quantity of data to be entropy encoded. At the decoding end, the point cloud geometric information may be incorporated to supplement the deleted 0 elements and the quantisation matrix may be restored according to the traversal sequence, thereby improving compression performance without introducing new errors.

Abstract: The application discloses a method, system, device and computer-readable storage medium for inverse quantization, wherein, in some embodiments, determining whether the inverse transform coefficient corresponding to the quantized coefficient in the quantized block can be set to 0, according to the size of the quantized block; when the inverse transform coefficient corresponding to the quantized coefficient can't be set to 0, the quantized coefficient is inverse quantized to obtain a corresponding inverse transform coefficient.

Abstract: An embodiment of the present disclosure discloses an intra-prediction-based encoding and decoding method, apparatus and filter, and belongs to a video coding technical field.

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: 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: 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, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on occupancy data corresponding to one or more parent nodes and one or more child nodes within the received data. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

Abstract: The present specification discloses a method, apparatus, and device for video frame interpolation.

Abstract: A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. Hash elements corresponding to one or more neighboring nodes associated with a current node are identified. A size of a hash table is decreased based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes. The data corresponding to the point cloud is decoded based on the hash table having the decreased size.