14 results about "Interframe correlation" patented technology

According to the method, the system, the medium and the equipment for monitoring the abnormal state of the wind generating set, a self-adaptive noise reduction mechanism, feature extraction and model classification are combined, so that physical information features are effectively extracted, and accurate state detection is achieved. The method comprises the following steps: firstly, building a signal acquisition system, acquiring an acoustic signal in real time, and executing direct current offset removal preprocessing to eliminate low-frequency noise; secondly, noise reduction of the original sound signal is completed by using a self-adaptive noise reduction method; then, extracting the MFCC characteristics of the noise reduction signal; then, calculating an MFCC inter-frame correlation feature and an aerodynamic-harmonic imbalance factor, and forming a fusion feature vector; and finally, constructing a physical neural network model based on physical information to carry out abnormal state detection.

One or more methods, apparatuses, computer-readable storage mediums, and systems for entropy coding vertex information of an edge in a voxelized space of a point cloud are disclosed. Symbols of a neighborhood configuration of a current edge may be determined based on one or more already coded edges. The already coded edges may be selected from a spatial topology of edges. The use of a motion-compensated point cloud for coding centroid residual values may enhance interframe correlation used for determining a context or probability model. This increased correlation may improve the selection of coders, leading to enhanced compression of centroid residual values.

The application provides a compression artifact suppression method combined with multi-level interframe correlation. The method mainly relates to a means combining multi-level motion compensation and deep convolution mapping to perform quality enhancement on HEVC decoded video. The application mainly utilizes the characteristics of interframe correlation of the video, and then designs a multi-level motion compensation network to perform motion compensation on two adjacent frames of a current frame, and then fuses features of the current frame with the two adjacent frames. Then, attention feature extraction is performed on the current frame and the fused multi-frame feature map respectively. Finally, deep residual learning and cross-layer cascade are performed to realize deep fusion, mapping and reconstruction of multi-level features, so that quality enhancement of the current frame is realized.

The application provides a coding method and device in AIGC generation. The decoding method in AIGC generation of the application comprises the following steps: obtaining first feature information and motion information of a current frame based on generated feature information output by an AIGC generation model, wherein the motion information is used to represent inter-frame correlation; performing motion modeling according to the first feature information and the motion information of the current frame to obtain second feature information of the current frame, wherein the motion modeling is used to compensate the first feature information of the current frame based on the motion information; and obtaining reconstructed content of the current frame according to the second feature information of the current frame. The application can improve the time domain compression efficiency and the quality of the reconstructed content in AIGC.

Various embodiments include techniques for performing hardware-assisted weighted prediction encoding with software-generated weight prediction parameters for a media frame. The disclosed weighted prediction encoding techniques take advantage of temporal interframe correlation between adjacent media frames in a video stream. In certain examples, a media frame, or portions thereof, can exhibit a rapid luminance change, such as during a fade in from black effect, a fade out to black effect, a video depicting an explosion or blast, and / or the like. This rapid luminance change reduces interframe correlation, even in cases where the interframe correlation would be higher if not for the luminance change. The disclosed techniques remove this luminance change before encoding, resulting in higher temporal correlation between adjacent frames. The higher temporal correlation can result in reduced bit rate and improved visual quality of the resulting output video stream.

The application discloses a space target trajectory correlation method and system, wherein the system comprises the following steps: obtaining target information detected by a detector in each calculation period; performing interframe trajectory correlation on the target according to the target information in each calculation period to obtain a trajectory correlation result, and storing the trajectory correlation in an interframe correlation target library; and obtaining target information of each frame from the interframe correlation target library, performing trajectory correlation on the target between frames, and obtaining target trajectory information of the current frame. The application effectively solves the problems of insufficient real-time performance, error accumulation and dimension explosion of traditional methods.

The application discloses a real-time multi-target detection system and method based on deep learning, and relates to the technical field of deep learning.The application utilizes interframe correlation multiplexing of overlapping area features through a space-time multiplexing module, thereby reducing redundant calculation; a feature enhancement module differentiates processing of dynamic and overlapping stable area features, strengthens basic dynamic features of a motion area, simplifies basic static features of an overlapping stable area, and performs weighted fusion on transition area features; and a window generation module dynamically adjusts detection window parameters based on target density.The application solves the problem that real-time performance and detection accuracy are difficult to be considered in the prior art, and improves processing efficiency and detection accuracy.

The application discloses a three-dimensional geometric model sequence compression method and device based on binary arithmetic coding, equipment and a storage medium, and the method comprises the following steps: segmenting a three-dimensional geometric model sequence based on the three-dimensional model data type of the three-dimensional geometric model sequence, obtaining a plurality of substructures, symbolizing the mesh patches of the three-dimensional geometric model with patch connection attributes, classifying the three-dimensional geometric model with vertex attributes based on interframe correlation, predicting the residual of the three-dimensional geometric model with vertex attributes based on the interframe correlation attributes, binarizing the quantized residual and / or the symbolized mesh patches, obtaining a binary bit string, and encoding the binary bit string, so that the three-dimensional geometric model sequence compression is realized, the multi-dimensional attributes of the dynamic three-dimensional geometric model sequence are effectively processed, the compression ratio and the compression efficiency are greatly improved, and the timeliness requirement of the three-dimensional geometric model sequence is met.

Provided in the present application are a coding method and apparatus during AIGC generation, and a decoding method and apparatus during AIGC generation. The decoding method during AIGC generation in the present application comprises: on the basis of generated feature information outputted by an AIGC generative model, obtaining first feature information of the current frame and motion information, wherein the motion information is used for representing inter-frame correlation; on the basis of the first feature information of the current frame and the motion information, performing motion modeling, so as to obtain second feature information of the current frame, wherein the motion modeling is used for performing compensation on the first feature information of the current frame on the basis of the motion information; and on the basis of the second feature information of the current frame, acquiring reconstructed content of the current frame. The present application can improve the time-domain compression efficiency and the quality of reconstructed content in AIGC.

The invention discloses a multi-channel filtering method for optimizing a full-band signal-to-noise ratio based on inter-frame correlation, and the method specifically comprises the steps: collecting a band correlation signal in a multi-channel voice system, and converting the band correlation signal to a time-frequency domain; generating a multi-channel vector signal containing inter-frame information; carrying out noise reduction processing on the multi-channel noisy voice signal containing the inter-frame information; calculating a correlation matrix of the multi-channel clean voice signal containing the inter-frame information and the multi-channel noise signal containing the inter-frame information; voice dominant and noise dominant subspaces are distinguished according to the sizes of the characteristic values, the voice dominant subspace with the maximum characteristic value is subjected to noise reduction and enhancement, and other noise dominant subspaces are directly set to zero; and forming a full-band filter with the length of 1 * 10. According to the method, the noise reduction stability, robustness and speech enhancement effect of the filter in a complex noise environment are remarkably improved. The invention further discloses a multi-channel filtering system for optimizing the full-band signal-to-noise ratio based on the inter-frame correlation.

One or more methods, devices, computer-readable storage media, and systems are disclosed for entropy encoding edge vertex information in a voxelized space of a point cloud. The symbols of the current edge's neighborhood may be determined based on one or more previously encoded edges. The previously encoded edges may be selected from the edge's spatial topology. The use of motion-compensated point clouds to encode centroid residual values ​​may enhance inter-frame correlation used to determine context or probability models. This increased correlation may improve coder selection and result in enhanced compression of the centroid residual values.

A video decoding method includes decoding an image bitstream associated with a video sequence, wherein the decoding of the image bitstream reconstructs a key reference frame; factorizing the reconstructed key reference frame into a key frame latent feature and a first group of compact motion vectors associated with the reconstructed key reference frame; decoding a feature bitstream associated with the video sequence to obtain a second group of compact motion vectors associated with an inter frame; transforming, based on the first group and second group of compact motion vectors, the key frame latent feature into a first fine-grained motion field for the reconstructed key reference frame and a second fine-grained motion field for the inter frame; predicting a dense motion based on the first and second fine-grained motion fields; and generating the inter frame based on the dense motion and the reconstructed key reference frame.

The invention discloses a real-time multi-target detection system and a real-time multi-target detection method based on deep learning, and relates to the technical field of deep learning. According to the real-time multi-target detection system and the real-time multi-target detection method based on deep learning, overlapping region features are multiplexed by utilizing inter-frame correlation through a space-time multiplexing module, and redundant calculation is reduced; the feature enhancement module carries out differentiation processing on dynamic and overlapping stable region features, reinforces basic dynamic features of a motion region, simplifies basic static features of an overlapping stable region, and carries out weighted fusion on transition region features; the window generation module dynamically adjusts detection window parameters based on the target density. According to the invention, the problem that real-time performance and detection precision are difficult to consider in the prior art is solved, and the processing efficiency and the detection accuracy are improved.

The invention discloses a nondestructive storage method of a fluorescence spectrum binary concentration image sequence based on multichannel bit mapping and entropy coding. Firstly, N frames of binary edge images with the same resolution are fused bit by bit according to pixels to form a fusion matrix V; and determining the storage bit width of the V, carrying out run length coding on the V, constructing a Huffman code table based on the statistical probability, carrying out entropy compression, and finally generating a compressed file. The header of the file records information such as the image size, the frame number, the data type and the CRC check code, and the integrity and the decompressibility of compressed data are ensured. And during decompression, performing Huffman decoding, inverse RLE and bitwise disassembly according to an inverted sequence to realize complete lossless recovery of an original sequence. By using the method, redundancy among multiple frames of images can be fully utilized, the compression ratio is improved, lossless reconstruction of original data is realized, and the method is particularly suitable for a binary image sequence scene with strong inter-frame correlation. In a multi-frame related scene, the compression ratio of the method is obviously superior to that of a traditional frame-by-frame PNG method, and the reproduction degree reaches 100%.