40586results about "Digital video signal modification" patented technology

An exemplary image segmentation method and system that employs, in a deep neural network, (i) an attention gate that fuses features with skip connections and (ii) a graph or multi-scale convolutional attention module that enhances the long-range and local context. The segmented region or image data derived in part from the segmented region can be subsequently employed for diagnosis, controls, planning, assessment, or analysis of various diseases. The image data can be medical images (e.g., from medical instruments), sensor images or videos (e.g., from smart phones, cameras, satellites, etc.), as well as 3D objects or volume.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

By implementing a new bitstream for a Delta-Quantization Parameter (DQP), a decoder is able to implement parallel decoding of multiple coding units within a largest coding unit. In some embodiments, the DQP is placed immediately after the mode information of the first coding unit. In some embodiments, the DQP is placed after the mode information of the first non-skipped coding unit. In some embodiments, the DQP is placed after the first non-zero coded block flag.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

A system and methods for processing diverse financial data types using a jointly trained Vector-quantized Variational Autoencoder (VQ-VAE) and neural upsampler. This system efficiently handles time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms and gated fusion units. The fused representation is compressed into a discrete latent space by the VQ-VAE encoder, then reconstructed and enhanced by the VQ-VAE decoder and neural upsampler, respectively. Joint training optimizes all components simultaneously, using a comprehensive loss function that balances reconstruction quality across modalities with upsampling performance. This approach enables superior data compression, reconstruction, and analysis, leveraging inter-modal correlations to improve financial forecasting, risk assessment, and decision-making. The system's ability to explore the latent space facilitates generation of new, synthetic financial scenarios for robust model testing and strategy development.

A system and methods for implementing adversarial-robust compression and reconstruction using a vector quantized variational autoencoder (VQ-VAE) with secure latent space management. The system provides comprehensive protection against adversarial attacks through multi-channel threat detection, adaptive defensive parameters, and coordinated response mechanisms. Input data is continuously monitored for potential threats, and defensive parameters are dynamically adjusted based on detected threat levels. The system implements bounded constraints and hierarchical projections to maintain latent space security while preserving compression efficiency. Multi-stage reconstruction with progressive validation ensures reliable data recovery even under adversarial conditions. The system coordinates defensive responses across all compression and reconstruction processes, implementing various recovery mechanisms when security violations are detected. This approach enables robust compression and reconstruction of time-series data while maintaining protection against various forms of adversarial manipulation.

The invention relates to the technical field of image processing, in particular to a method for transmitting real-time pictures of ultrasonic equipment, which comprises the following steps of: acquiring an ultrasonic image through the ultrasonic equipment, dividing the acquired image into block images, and analyzing each block image by using a spatial information analysis model; the method comprises the following steps: identifying a key region and a non-key region in an image through a deep learning model, setting different compression strategies, performing time sequence analysis on acquired bandwidth data by using a bandwidth prediction model, and adjusting a compression ratio, a resolution ratio and a frame rate of the images of the key region and the non-key region according to a prediction result. According to the method, compressed image data is split into a plurality of data streams, a path with high transmission quality is preferentially selected for transmission, multi-stage reconstruction is performed on ultrasonic image data at a receiving end, an image of a key area is processed by using a super-resolution reconstruction algorithm, and high-frequency noise is removed by using a hierarchical denoising algorithm and a convolutional neural network. According to the invention, the real-time performance and reliability of image transmission are improved.

The present disclosure provides a variable-bit-rate image compression method and system, an apparatus, a terminal, and a storage medium. The variable-bit-rate image compression method includes: obtaining an initial feature map from a to-be-encoded image; quantizing the initial feature map by a dead-zone quantizer; performing entropy encoding on the quantized feature map and hyper-prior information to obtain a compressed bit-stream; performing entropy decoding on the compressed bit-stream, and recovering quantized hyper-prior information and the quantized feature map; performing inverse quantization on the quantized feature map to obtain a reconstructed feature map; obtaining a reconstructed image from the reconstructed feature map; and adjusting quantization and inverse quantization parameters according to a target bit-rate or target distortion. The present disclosure provides a precise bit-rate control solution, makes the bit-rate of the compressed bit-stream better adapt to the dynamic change of a network bandwidth, and has an extremely high actual application value.

The invention discloses an audio and video media transmission method and system, and relates to the field of data transmission, and the method comprises the steps: obtaining video frame data; performing motion analysis on the video frame data to obtain a first region, a second region and a third region; coding the video frame data of the first region by adopting an inter-frame prediction coding mode; coding the video frame data of the second area by adopting a motion compensation coding mode; coding the video frame data of the third region by adopting an intra-frame prediction coding mode; coding the motion vector of the second region to obtain fourth coded data; performing code rate control and bit stream encapsulation according to the first coded data, the second coded data and the third coded data to obtain a video code stream; and combining the video code stream and the fourth coded data to obtain coded video transmission data, and transmitting the coded video transmission data. Aiming at inflexible code rate control in video coding in the prior art, the invention improves the code rate control flexibility.

The invention provides a large-scale scene multi-level-of-detail cloud rendering processing method and device based on 3DGS, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: dividing a target modeling scene into a plurality of sub-blocks; performing particle redundancy reconstruction and overlapping region marking on boundary regions between adjacent sub-blocks of each sub-block to obtain processed sub-blocks; performing multi-detail level division on each processing sub-block to generate a particle level set; determining a current visual area according to the user motion data, and scheduling a target hierarchy of a particle hierarchy set in the current visual area; the rendering tasks of all the processing sub-blocks of the target hierarchy are distributed to a plurality of rendering nodes to execute real-time rendering operation; and performing video stream coding on pictures rendered by each rendering node, decoding and displaying received video stream data, and performing particle level updating and re-rendering operation according to an interaction instruction. According to the invention, high-quality detail rendering can be realized for a model of a large scene.

The invention relates to the technical field of streaming media decoding, and discloses a streaming media decoding method and device for a network high-definition player and a storage medium, and the method comprises the steps: carrying out the multi-thread parallel collection and storage of real-time temperature data, network bandwidth jitter data and video code rate fluctuation data of a decoding chip in the network high-definition player; obtaining a streaming media dynamic feature data set; performing classification training to obtain an inter-frame correlation prediction model; constructing a multi-stage streaming media caching system based on the inter-frame correlation prediction model; monitoring a cache state, a network state and a chip state in the multi-stage streaming media cache system in real time to obtain a decoding event sequence; according to the decoding event sequence, the decoding parameter is adjusted in real time, and the streaming media decoding control instruction is obtained.The collaborative optimization of a network layer, a decoding layer and a display layer is achieved, the playing starting delay is reduced, and the overall stability of a network high-definition player is improved.

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

The invention relates to the technical field of image communication, in particular to a computer network communication data processing method and system based on artificial intelligence, and the method comprises the following steps: carrying out the continuous frame difference calculation of the data of real-time video frames, collecting the pixel-level change data between two frames, and obtaining a difference data set; according to the method, continuous frame difference calculation is carried out on real-time video frames, pixel-level change data between two frames is captured, storage and transmission occupation of useless data is reduced, and data processing pressure is relieved. And the compression of the difference data further optimizes the size of the data packet, so that the transmission efficiency is improved, and the integrity of video key content is guaranteed at the same time. The compressed difference data packets are analyzed, key visual elements are extracted, and limited transmission resources are distributed to important information parts in a centralized manner by focusing on visual key points such as human faces and actions, so that the pertinence of the transmission process is enhanced.

An apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine information related to a usability of at least one image with an artificial intelligence model; and signal the information related to the usability of the at least one image with an artificial intelligence model.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

Systems and methods are described for video coding using generalized bi-prediction. In an exemplary embodiment, to code a current block of a video in a bitstream, a first reference block is selected from a first reference picture and a second reference block is selected from a second reference picture. Each reference block is associated with a weight, where the weight may be an arbitrary weight ranging, e.g., between 0 and 1. The current block is predicted using a weighted sum of the reference blocks. The weights may be selected from among a plurality of candidate weights. Candidate weights may be signaled in the bitstream or may be derived implicitly based on a template. Candidate weights may be pruned to avoid out-of-range or substantially duplicate candidate weights. Generalized bi-prediction may additionally be used in frame rate up conversion.

A method implemented by a video coding apparatus. The method includes applying a neural network (NN) filter to an unfiltered sample of a video unit to generate a filtered sample. The NN filter includes an NN filter model generated based on partitioning information of the video unit. Usage of the NN filter is indicated by one or more syntax elements in a bitstream. A conversion is performed between a video media file and a bitstream based on the filtered sample.

The present invention relates to the technical field of cross-modal image signal reconstruction. Disclosed is a cross-modal joint source-channel coding and decoding method adaptable to changeable scenarios. The method comprises: first designing a Transformer encoder-based cross-modal channel coding and decoding optimization solution, so as to achieve the performance improvement and robustness of a channel encoder and a channel decoder; then designing a cross-modal source coding and decoding optimization solution for haptic-to-image generation based on a latent diffusion model, so that under an image signal loss scenario, haptic information is used to guide image generation; and finally, incorporating transfer learning technology, so as to reduce additional training costs caused by a system facing changeable cross-modal communication scenarios such as a changeable channel signal-to-noise ratio and different transmission tasks. Under cross-modal changeable communication scenarios, the joint source-channel coding and decoding method provided in the present invention can solve the problems of the inability of a receiving end to well complete image reconstruction, and additional model training costs caused by changeable channel environments and scenarios.

The disclosure relates to a data processing method and a related device for point cloud media. The method includes: obtaining a media file of point cloud media, the media file comprising a point cloud bitstream and cross-attribute dependency indication information of the point cloud media, and the cross-attribute dependency indication information being for indicating an encoding and decoding dependency relationship between attribute data in the point cloud bitstream; and decoding the point cloud bitstream based on the cross-attribute dependency indication information to present the point cloud media.

A method for processing media data is disclosed. In an embodiment, the method includes determining a value of a neural-network post-filter characteristics (NNPFC) purpose (nnpfc_purpose) in a NNPFC supplemental enhancement information (SEI) message, wherein the nnpfc_purpose is configured to be set to include output picture size downsampling, and wherein at least one of (1) an output picture width is not equal to an input picture width and (2) an output picture height is not equal to an input picture height; and performing a conversion between a visual media data and a bitstream based on the nnpfc_purpose.

A generalizable neural radiation field reconstruction method based on multi-modal information fusion, including: Step 1, constructing photometric features and geometric features based on unstructured multi-views, and constructing a multi-modal neural encoder by performing incrementally complementary fusion on the photometric features and the geometric features; Step 2, converting the multi-modal neural encoder and raw RGB pixel bodies of the unstructured multi-views into a volume density and radiation brightness; Step 3, sampling light on the basis of the constructed multi-modal neural encoder, aggregating context features of the sampled light based on a transformer network to obtain light context features; and Step 4, decoding, using the light context features, the volume density and the radiation brightness; rendering, based on the decoded volume density and the radiation brightness, to generate a free-view RGB-D image; and guiding dense reconstruction of a low-texture scene by combining photometric supervision and sparse geometric supervision.

The invention discloses a system for improving the applicability of an intelligent camera, and relates to the technical field of intelligent camera shooting. RGB, infrared and depth sensors are used for cooperative sensing, a dynamic inter-frame interpolation algorithm is used for complementing video data, target trajectory prediction is carried out based on a target motion vector analysis algorithm in combination with a recurrent neural network LSTM + Kalman filtering, and the applicability of the intelligent camera is improved. Meanwhile, three-dimensional scene modeling and shielding recovery are introduced, and environment information complementation of a dead angle area is optimized; through a cross-camera target identity re-identification ReID algorithm, seamless tracking is carried out among multiple cameras by utilizing appearance characteristics, gait characteristics and biological characteristics of a target, and meanwhile, PTZ camera control is optimized in combination with reinforcement learning, and the angles of the cameras are dynamically adjusted; a generative adversarial network GAN based on space-time consistency is adopted, historical video data and environment modeling are combined, missing pictures in visual dead angles are intelligently complemented, and the continuity and authenticity of monitoring pictures are improved.

Ways to post-process a decoded mesh and modify received triangles per patch to improve the mesh geometry are described herein. Since the transmitted geometry contains the high-resolution surface information, the information is able to be applied to the mesh reconstruction operation to generate triangles that are aligned with the original surface. Methods include generating new triangles by splitting the received triangles' edges according to their size, by inserting new vertices at the triangle's centroids, by splitting the vertices, and by performing marching cubes in surfaces defined by the geometry images.

A method of encoding or decoding video data comprises: for each respective intra prediction mode of a plurality of intra prediction modes in a most-probable mode (MPM) list: generating, based on reference samples for a template region and using the respective intra prediction mode, prediction samples for the template region; and determining a cost for the respective intra prediction mode; determining a first intra prediction mode and a second intra prediction mode in the MPM list having lowest costs; determining a preliminary prediction block for the first intra prediction mode and a preliminary prediction block for the second intra prediction mode; generating a prediction block based on a fusion of the preliminary prediction blocks weighted according to a weight for the first intra prediction mode and a weight for the second intra prediction mode.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

The invention provides an unmanned aerial vehicle multi-channel image transmission optimization system based on link quality perception so as to improve the transmission stability and the image quality guarantee capability of image data in a complex wireless environment. The prediction module constructs a time sequence model based on the historical link quality index of the wireless channel, and outputs a future link stability prediction value; the modeling module analyzes texture change and the like between the image frames, generates an image frame evolution vector, and calculates the aging sensitivity and reconstruction importance score of the image frames according to the image frame evolution vector; the decision-making module fuses the information and generates an optimal matching relation between the image frame and the channel and a transmission priority parameter; the grouping module performs image frame clustering according to the similarity between the priority parameters and the evolution vectors, constructs compression groups and generates corresponding compression configuration files and compression image data; the transmission module schedules the compressed image data to a corresponding wireless channel for transmission; the system can be widely applied to an unmanned aerial vehicle image transmission task with relatively high requirements on real-time performance and image quality in a dynamic environment.

Disclosed are an encoding / decoding method, apparatus, and system. In an implementation, the encoding method includes: encoding video information, where the video information includes M frames, the M frames include a first frame, a second frame, and a third frame, the second frame refers to the first frame, and the third frame refers to the second frame or the first frame, storing the first frame, the second frame, and the third frame in a buffer to obtain candidates of a long-term reference frame, and selecting a subset from the candidates based on a feedback signal as a long-term reference frame.

The invention discloses a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

There is provided methods and apparatus of encoding / decoding a point cloud representing a physical object. Points are captured by a spinning sensors head and are represented by sensor indices associated with sensors that captured the points, azimuthal angles representing capture angles of said sensors, and radius values of spherical coordinates of the point. Points are ordered based order indices obtained from the azimuthal angles and the sensor indices. Order index differences are encoded. An order index difference represents a difference between order indices associated with two consecutive ordered points. Optionally, the method encodes radius values, residual azimuthal angles associated with ordered points and residuals of three-dimensional cartesian coordinates of ordered points based on their three-dimensional cartesian coordinates, decoded azimuthal angles based on azimuthal angles, decoded radius values and sensor indices.