2912 results about "Coding decoding" patented technology

A mesh and point cloud coding method and an apparatus predict with reference to a reconstructed point cloud in encoding / decoding a mesh to increase coding efficiency for three-dimensional meshes and point clouds. Alternatively, the mesh and point cloud coding method and the apparatus predict with reference to a reconstructed mesh in encoding / decoding a point cloud.

The invention discloses a video semantic segmentation method based on a time sequence cross attention mechanism, and belongs to the field of computer vision and the field of material detection.The video semantic segmentation method comprises the steps that firstly, a video used for training is preprocessed, a frame sequence is extracted, and then a coding-decoding network for multi-level feature extraction and fusion is constructed; according to the method, feature extraction is enhanced through a time sequence cross attention module, network parameters are optimized through weighted IoU loss and binary cross entropy BCE loss, then frame-by-frame prediction segmentation is carried out on a target video by using a trained model, and a multi-classification segmentation result is exported. According to the method, a time sequence cross attention mechanism is integrated into the SAMUNet network, the segmentation precision is effectively improved for image data with time sequences, the time cost and the labor cost of material video processing are greatly reduced, the method can be widely applied to the field of industrial detection, and the product quality and the production efficiency are improved.

The invention belongs to the technical field of smart ocean and computer vision, and discloses a cross-modal interaction-based ocean ship image-text positioning method, which comprises the following steps of: constructing a data set; constructing a coding-decoding structure model; extracting a visual feature # imgabs0 # and a linguistic feature # imgabs1 #, and generating a visual feature # imgabs2 # and a linguistic feature # imgabs3 #; fusing the above features to generate a coordinate prior # imgabs4 # and an alignment feature # imgabs5 #, enhancing an object query # imgabs7 # by using # imgabs6 #, and interacting with a multi-modal visual feature # imgabs8 # and a language feature # imgabs9 #; calculating a loss function value, and training for multiple rounds to obtain a final model. According to the method, the single-modal features are associated with other modal features, so that the semantic understanding of the model on visual and language features is improved; coordinate prior is utilized to enhance object query, a discrimination fusion module is introduced to enhance semantic consistency of multi-modal features, and finally efficiency and accuracy of a ship image-text positioning task are effectively improved.

The invention provides a multi-modal automatic driving scene generation method based on autoregressive closed-loop prediction. The method comprises the following steps: encoding an automatic driving scene video, scene understanding prediction questions and answers and trajectory planning data into a unified multi-modal discrete code by using a pre-trained discrete encoder; multi-modal discrete codes are integrated into a coding sequence, a mask strategy is adopted to cover trajectory codes, original codes without masks serve as a supervision target, a model is generated through autoregressive normal form training, and fine tuning is carried out on scene understanding, scene prediction and trajectory planning data sets; during reasoning, an initial scene image collected by a vehicle camera and a user prompt word are input, the model generates a future scene discrete coding sequence according to initial information, and closed-loop prediction is realized through mask track information; the generated codes are decoded into scene images, text questions and answers, and vehicle trajectories using a symmetric decoder. According to the invention, the problems of poor universality and low efficiency of the existing end-to-end automatic driving world model can be solved.

The invention relates to the technical field of commodity anti-counterfeiting, in particular to an intelligent identification and analysis method for an anti-counterfeiting mark, which comprises the following steps: firstly, acquiring environmental parameters in real time, acquiring a holographic image of the anti-counterfeiting mark through a multispectral imaging technology, and correcting the image by using environmental data to generate a standardized hologram; then microcosmic anti-counterfeiting features in the dynamic dot matrix hologram are extracted to construct holographic feature vectors; and synchronously collecting encrypted bar code data, dynamically adjusting an error correction parameter and a decoding threshold value of a bar code decoding algorithm through the environment compensation model, and obtaining a first optimized bar code decoding algorithm for decryption. And checking the aging matching between the hologram timestamp and the bar code dynamic key by using an asymmetric encryption algorithm, and calculating the matching degree between the real-time holographic feature vector and a pre-stored standard vector by using a feature similarity algorithm. And when the matching degree is greater than a preset threshold value and the dynamic key verification is passed, determining that the product is a genuine product and updating the dynamic key.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention provides a lightweight small target detection method based on a feature sparse converter, and aims to improve the efficiency and precision of small target detection through model lightweight. According to the method, the small target image data set is constructed, and the feature sparse converter model is used for detection, so that the calculation complexity of the model is effectively reduced, and the light weight of the model is realized. The detection model comprises an image feature extraction module, a feature coding and decoding module, an image detection frame generation module, a feature rarefaction module and the like, redundant features are reduced by introducing a rarefaction mechanism, the calculation efficiency is improved, and the calculation resource demand of the model is remarkably reduced while high detection precision is kept. Experimental results show that the method has a good application prospect in small target detection tasks in a complex environment, and is suitable for practical application scenes with limited resources.

The invention provides an image coding and decoding method and a compression method for machine and human vision, and the method mainly comprises the steps: obtaining a first feature map and a second feature of a to-be-coded image, and obtaining the super-prior information of the first feature map; obtaining quantization step sizes of the to-be-coded image under different machine vision tasks, and performing quantization to obtain a corresponding feature map; modeling the feature map into Gaussian distribution, and predicting a corresponding mean value and variance through an entropy model; coding layer by layer according to the distribution and probability of each layer of feature map to obtain a feature map binary code stream; and combining the super-prior information binary code stream and the feature map binary code stream to obtain a compressed image binary code rate. According to the method, the quantization step size of feature map quantization is adaptively adjusted, transmission feature code streams for human vision and different machine vision tasks are formed, information in the feature map can be more efficiently utilized, the transmission code rate is reduced, meanwhile, the performance of the machine vision tasks is guaranteed, and meanwhile, the rate distortion performance oriented to human vision is balanced.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. In typical image encoding / decoding methods, a decoder-side motion information derivation method may be limitedly used. Therefore, the improvement of encoding efficiency attributable to the decoder-side motion information derivation method may also be limited. In embodiments, a motion information search method used in an inter-prediction mode, an intra block copy mode and an intra template matching prediction mode is disclosed. With the use of various motion search methods, encoding efficiency may be improved.

Disclosed herein are a method, an apparatus, and a storage medium for image encoding / decoding. An intra-prediction mode for the target block is derived, and intra-prediction for the target block that uses the derived intra-prediction mode is performed. The intra-prediction mode for the target block is derived using an artificial neural network, and an MPM list for the target block is derived using information about the target block, pieces of information about blocks adjacent to the target block, and the artificial neural network. The artificial neural network outputs one or more available intra-prediction modes. Further, the artificial neural network outputs match probabilities for one or more candidate intra-prediction modes, and each of the match probabilities for the candidate intra-prediction modes indicates a probability that the corresponding candidate intra-prediction mode matches the intra-prediction mode for the target block.

The invention relates to the technical field of backscatter communication, and discloses a backscatter method for any multi-frequency Bluetooth carrier, which comprises the following steps: a carrier device can send out carrier signals of multiple frequencies; the label modulates label data into a carrier signal to generate a backscattering signal; an edge device identifies a serial number of a corresponding connection event through a data packet in a specific data channel and sends the serial number of the connection event to a label, and the label calculates a corresponding data channel serial number according to the serial number of the connection event, so that the label can reflect carrier signals of different data channels to a fixed channel; identification fields are arranged in data packets corresponding to the carrier signals and the backscattering signals, and label data are decoded based on identification field contents and load contents of the carrier signals and the backscattering signals. The label can accurately control the reflection channel, and meanwhile label data transmission is supported through a provided coding and decoding mechanism.

An image encoding / decoding method and device according to the present disclosure can induce an intra-prediction mode of the current block on the basis of an induction mode, determine a reference sample of the current block, and generate a prediction sample of the current block on the basis of the intra-prediction mode and the reference sample.

The invention discloses a stereo image quality evaluation method of a multi-task coder-decoder based on Transform, and belongs to the technical field of image quality evaluation. The invention provides a multi-task coding-decoding device network architecture based on Transform. The multi-task coding-decoding device network architecture takes image depth estimation as an auxiliary task; a feedback mechanism of a human visual system can be better simulated by adopting a coder-decoder network, the problems existing in left and right view feature extraction are solved, and an information processing process from a global state to a local state is realized; in addition, the global interaction capability of Transform and the local attention capability of CNN are combined to enhance image features, depth estimation is introduced as an auxiliary task, depth prior information with higher quality can be provided, cross-view parallax fusion based on Transform is guided, the problem existing in left and right view feature fusion is solved, and more accurate binocular features are obtained. And feedback compensation is carried out on multi-level binocular feature fusion by using an attention mechanism to obtain a final pixel-level quality map, so that the image quality is accurately evaluated, a research scheme is provided for a deep learning method for non-reference stereo image quality evaluation, and the development of a visual task is effectively promoted.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. Methods for performing more accurate prediction are disclosed when performing prediction using a block vector. A block vector for a target block is derived by template matching, and a prediction block for the target block is determined based on the derived block vector. Various and detailed embodiments for a plurality of pieces of information used to derive a block vector, such as a search area, a block vector candidate list, a reference region, a buffer, and a tree type, are provided.

To provide a moving image coding and decoding device capable of improving a coding efficiency.SOLUTION: A moving image decoding device comprises a parameter decoding part that decodes a syntax (mts_idx) of an object block from a coding data. The parameter decoding part is characterized that the mts_idx is decoded in accordance with a block size of the object block, a LAST position, and a non-zero coefficient position. The parameter decoding part determines whether or not the mts_idx is decoded or not by using a threshold value of the LAST position. The threshold value is characteristic that, in the case where the block size is a predetermined size or larger, the block size is larger as compared with the case where the block size is smaller than the predetermined size.SELECTED DRAWING: Figure 15

The invention relates to a machine tool thermal error modeling method based on a time convolution network and transfer learning, which comprises the following steps: data acquisition: providing a network based on an encoder-decoder architecture based on the time convolution network, inputting the acquired data of source (sufficient) and target (few) machine tools into an encoder, and carrying out data processing on the encoder; the method comprises the following steps of: coding and decoding to output a reconstruction sequence, and adding a reconstruction loss function which is calculated by the reconstruction sequence output by a decoder on a loss function with only one predicted value, so that deep features in thermal error data can be extracted through the architecture, and the prediction precision is improved; in order to achieve enough prediction precision on another target machine tool, transfer learning is used to obtain a loss function adapted to a field, the loss functions are summarized, iterative updating is repeated until deep learning regression algorithm training is completed, and accurate prediction of the thermal error value on the target machine tool is realized.

A robotic controller for controlling a robot according to a sequence of robotic actions. comprises an input interface configured to receive a plurality of multimodal inputs each specifying instructions for performing a task in a different modality including audio, video, and a text modality. The controller also comprises a multimodal large language model, an action sequence decoder, and a controller. The multimodal LLM includes a multimodal LLM encoder and an LLM decoder. The multimodal LLM encoder is trained with machine learning to transform the multimodal instructions into encodings and the LLM decoder is configured to decode the encodings into a sequence of robotic instructions. The action sequence decoder is trained with machine learning to transform the sequence of robotic instructions into a sequence of actions using a library of robotic skills. The controller is configured to control a robot according to the sequence of actions.

Apparatuses and methods of manufacturing same, systems, and methods for performing network parameter quantization in deep neural networks are described. In one aspect, multi-dimensional vectors representing network parameters are constructed from a trained neural network model. The multi-dimensional vectors are quantized to obtain shared quantized vectors as cluster centers, which are fine-tuned. The fine-tuned and shared quantized vectors / cluster centers are then encoded. Decoding reverses the process.

This disclosure provides an image encoding / decoding method and device and recording medium storing a bitstream. The image decoding method may comprise: constructing a candidate list related to prediction of a current block; generating a prediction block of the current block based on a candidate selected from the candidate list; and generating a reconstructed block of the current block based on the prediction block, wherein the constructing of the candidate list includes adding candidates to the candidate list in an order of a first candidate serving as a spatially neighboring block, a second candidate serving as a temporally neighboring block, and a third candidate serving as a non-neighboring block.

The invention provides an image coding method and device based on deep learning, and belongs to the field of image coding, and the image coding method comprises the steps: a coding end uses nonlinear transformation to project an original image to a hidden space; the coding end performs probability distribution estimation by using a causal context; the encoding end encodes the hidden variables by using an entropy encoding technology; the decoding end performs probability distribution estimation by using a causal context; the decoding end decodes the hidden variables by using an entropy coding technology; the coding end reconstructs the hidden variables into an original image by using nonlinear transformation; and performing end-to-end reverse gradient propagation optimization on an image coding and decoding process by using a distortion loss function and a causal context adjustment loss function. By using a better causal context to adjust a loss function and a more efficient network structure, better image compression capability and higher rate distortion performance are obtained.

The invention provides a segmented cable damage detection method based on a Retinex theory. The segmented cable damage detection method comprises the following steps: S1, inputting a low-illumination cable image; s2, preprocessing the image data; s3, using a Retinex module to enhance the brightness feature and the contrast feature of the image; s4, extracting high-frequency features of the image by adopting a variational mode decomposition (VMD) module; s5, performing high-frequency modal fusion to generate an enhanced image with richer details; s6, adopting a void space pyramid pooling ASPP module to perform void convolution processing on the brightness features, the contrast features and the high-frequency features, and fusing the features on the basis of multi-scale feature extraction; and S7, a deep learning segmentation network of a SegNet architecture is adopted, and the segmentation effect is optimized through a coding-decoding structure of the deep learning segmentation network. According to the method, the problems of insufficient image brightness and low contrast in cable damage inspection under a low-light condition are solved, and details of cracks and damage can be accurately identified; the limitation of a traditional detection method is overcome, and the situations of false detection and missing detection are effectively reduced.

The invention discloses a thunder approaching prediction method and device based on multi-source meteorological data, and particularly relates to the technical field of thunder disaster prediction.The method comprises the steps that standardization processing of temporal-spatial resolution unification is conducted on meteorological satellite data, radar data and lightning positioning data, and standardized temporal-spatial input data is generated; then spatial-temporal features are extracted through a depth separable 3D convolution module, and a compressed spatial-temporal feature graph is generated; then, a channel-space double attention mechanism (CPCA) is applied to the compressed feature map for feature optimization; and finally, processing the optimized feature map through a coding-decoding structure, and outputting a thunder and lightning probability distribution map. According to the method, the problem of spatial-temporal resolution difference and physical feature mismatching in multi-source data fusion is innovatively solved, the calculation efficiency is remarkably improved through a lightweight network architecture, the feature expression ability is enhanced by using an attention mechanism, and high-precision prediction of sudden thunder and lightning events is realized.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

The invention relates to the technical field of short-term load prediction in a power system, and discloses a short-term load prediction system based on a coder-decoder architecture, and the system is characterized in that a coder is used for extracting local features of a power load mode; and the decoder is used for converting the local features of the power load mode into predicted power load values and outputting the predicted power load values. The encoder is realized by a multi-scale expansion causal convolutional network MSDCC, and the decoder is realized by a bidirectional long short-term memory network BiLSTM. The prediction system construction method comprises the following steps: extracting related data from a historical database, preprocessing and analyzing the data, and constructing a predictor matrix; an MSDCC encoder is constructed; a BiLSTM decoder is constructed; and combining the encoder-decoder architecture to construct a short-term load prediction model. According to the method, the size of the feature map is effectively limited, model parameters are reduced, overfitting is avoided, calculation requirements are controlled, non-linear features are efficiently captured, meanwhile, time keeping complexity is low, and therefore the method has the advantages of being high in prediction efficiency and high in prediction precision.

An encoding method includes first obtaining a to-be-encoded picture block, then determining encoding complexity of the to-be-encoded picture block based on a plurality of prediction modes, next determining a quantization parameter of the to-be-encoded picture block based on the encoding complexity, and after that encoding the to-be-encoded picture block based on the quantization parameter to generate a bitstream. The plurality of prediction modes include at least one of block-level prediction, point-level prediction, and intra block copy prediction, and the encoding complexity indicates encoding difficulty of the to-be-encoded picture block.

A medical image segmentation method based on multi-extended recursive attention belongs to the technical field of image segmentation, and comprises the following steps: a coding part: coding input features by a multi-extended recursive attention block and a feature merging layer; in the decoding part, firstly, the feature data of the multi-extended recursive attention block encoding part are input into a decoder; secondly, the decoder gradually recovers the original resolution of the image through up-sampling, and semantic information obtained through up-sampling and features output by the encoder of the first part are subjected to feature interaction through jump connection; thirdly, inputting the interacted features into the decoding part of the recursive attention block, and restoring the original information of the image to the maximum extent; and finally, through a linear mapping layer, obtaining a pixel-level segmentation prediction result according to the extracted image feature marks. By adopting the method, the model can show better overall performance and robustness in a segmentation task, and the method has important significance in practical application in the fields of medical images and the like.