144 results about "Adaptive video" patented technology

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.

The invention provides an intelligent self-adaptive video editing method and system based on deep learning, and relates to the technical field of video processing.The method comprises the steps that firstly, a semantic mapping relation between a to-be-edited video material and a preset editing requirement is established, and an editing requirement mapping result is generated, the preset editing demand comprises a content style and a rhythm control demand, and then semantic feature association processing is carried out based on the mapping result to obtain a semantic association feature set comprising lens unit content semantic features and rhythm association features; then calling a pre-trained editing decision model (including a semantic matching module and a rhythm adjusting module) to carry out editing strategy matching on the set, generating a preliminary editing strategy set, generating an initial video editing scheme according to the preliminary editing strategy set, carrying out parameter adjustment on the initial scheme according to a strategy optimization suggestion output by the model, and carrying out video editing on the initial scheme; and a final video editing scheme is obtained, and intelligent self-adaptive editing of the video is realized.

The invention discloses a multi-mode large model video content understanding reasoning acceleration method and system, and mainly relates to the technical field of artificial intelligence reasoning acceleration. Comprising the following steps: inputting video data and preprocessing the video data to generate a video frame sequence; performing adaptive video Token compression on the generated video frame sequence, and outputting a compressed visual Token set; performing visual feature coding and Key-Value generation on the compressed visual Token set to obtain visual KV data; performing video KV cache partition management on the visual KV data; executing cross-modal reasoning based on the vLLM framework to generate a video content understanding result; and outputting a video content understanding result, and carrying out post-processing and structured mapping. The method has the beneficial effects that the obvious reasoning acceleration and throughput improvement can be realized on the premise of keeping the precision of the original large model.

A computing system, a method and a computer program product for presenting a determined optimal duration of video recap of media content. The method includes detecting, via a processor of a computing system, selection of a current episode of media content for playback. In response to detecting selection of the current episode of the media content for playback, the method includes determining a first time difference between a current time and a previous viewing time of prior episodes. The method includes determining, based on the first time difference, a first time duration for a first video recap of the prior episodes of the media content. The method includes streaming the first time duration of the first video recap of the media content for presentation on an electronic device as a preview presented prior to streaming the current episode of the media content for presentation on the electronic device.

The invention discloses an adaptive video restoration method based on a digital video technology, and relates to the technical field of image communication. The method comprises the following steps: preprocessing an original video containing hard subtitles and automatically removing the hard subtitles to obtain a video to be repaired; screening a reference frame and a to-be-repaired frame by comparing the original video with the to-be-repaired video; determining a to-be-repaired area according to the pixel gray scale difference; extracting matched feature points, calculating a reference motion vector, estimating possible positions and motion differences of points in the to-be-repaired region in combination with object structure segmentation and region prediction, and generating a corrected motion vector; performing sub-pixel-level interpolation by using the vector, and constructing a corrected reference image; and finally, fusing the corrected image and the to-be-repaired area to generate a repaired video frame. The method can effectively improve the restoration quality of the subtitle shielded area.

The invention discloses a multi-modal large model adaptive video frame compression method and system, and relates to the field of multi-modal video analysis, and the method comprises the steps: S1, obtaining a user text instruction and a sampling video frame of an original video; s2, converting the user text instruction into a space-time semantic instruction through hierarchical thinking chain reasoning; s3, extracting visual features of the sampled video frames, and performing importance scoring on the visual features through a space-time semantic instruction to obtain a semantic weight matrix; and S4, based on the semantic weight matrix, dynamically adjusting the number of visual features and the spatial resolution of each frame, and based on the new spatial resolution, adjusting adaptive pooling parameters and performing adaptive weighted pooling to obtain compressed and refined features. According to the method, a user text instruction is decoupled into a time, space and context three-dimensional instruction, a dynamic semantic weight matrix is generated, and a vision-text semantic alignment error is reduced; the token density is adaptively adjusted based on the weight matrix, the redundant region is compressed and merged, and the calculation complexity is reduced.

The invention provides an information processing method and system based on unmanned aerial vehicle video spatialization, and relates to the technical field of unmanned aerial vehicle video processing. Firstly, camera optical parameters, camera pose information and sensor type identification are collected and synchronously coded to a non-display data segment of an unmanned aerial vehicle video frame structure to form a coded video with multi-dimensional space information, and then a coded video stream is formed through streaming packaging. And performing scene-based transcoding adaptation on the coded video stream to obtain an adaptive video stream, performing hierarchical decoding and splitting, constructing a dynamic space mapping model in combination with an image correction model, and calculating the geographic range of a video frame to obtain a live video with a multi-reference geographic range identifier. Based on the live video, geographic data with ground feature attributes and recognition confidence are generated and transmitted in a multi-link mode, a feedback modification track is received and layered updating processing is executed, bidirectional dynamic synchronization and conflict resolution of the geographic data and the live video are achieved, and the quality and reliability of geographic information are improved.

An apparatus comprising an interface and a processor. The interface may be configured to receive pixel data of an environment. The processor may be configured to process the pixel data arranged as video frames, generate a luminance distribution map of the video frames in response to a low-pass filter operation, determine a plurality of defogging intensity weights for the luminance distribution map, perform adaptive smoothing to each of the plurality of defogging intensity weights, and generate defogged video frames in response to the video frames and the plurality of defogging intensity weights with the adaptive smoothing. The plurality of defogging intensity weights may each correspond to one of a plurality of luminance intervals of the luminance distribution map. The adaptive smoothing may be configured to prevent brightness differences in the defogged video frames.

The invention relates to a high-fidelity generative video stream transmission system based on a visual base model, which belongs to the field of image communication, and is characterized in that a visual enhancement-oriented generative codec is designed, and high-fidelity video reconstruction under a high compression ratio is realized through an asymmetric space-time compression strategy and time sequence consistency enhancement; a resolution scaling module is provided, the calculation complexity is remarkably reduced through a video super-resolution recovery module of adaptive resolution control and joint optimization, and real-time high-definition video processing is achieved; and constructing a network adaptive video stream transmission controller, an intelligent token discarding mechanism based on semantic importance and a mixed packet loss processing strategy to realize code rate scalable control and robust transmission under network fluctuation.

The invention discloses a dynamic self-adaptive video black edge real-time detection method based on multistage scanning, and relates to the technical field of image processing, and the method comprises the following specific steps: 1, inputting a video frame, and selecting 28 detection regions in the video frame according to the requirements of four-corner region positioning, edge center region center positioning and cross region positioning; 2, sparse sampling is carried out on the 28 detection areas one by one, whether the detection areas are black areas or not is judged according to sampling results, if yes, the step 3 is executed, and if not, the detection areas are output; 3, preliminarily determining the orientation of the black edge of the video frame and the starting and ending positions of the black edge in the four-edge direction of the video frame; the method supports real-time updating of the position of the black edge, adapts to resolution switching, transverse and vertical screen change and picture cutting, realizes efficient and accurate black edge detection, and updates the detection result in real time. Meanwhile, the method supports a mainstream video format (RGB / YUV), and the false detection rate is lower.

The invention relates to the technical field of video decoding, in particular to a self-adaptive video low-delay hard decoding display system, which identifies a video stream format and a switching state through a self-adaptive demultiplexing module, extracts naked stream data and judges a decoding mode; the dynamic decoder allocation module dynamically allocates decoding resources according to the mode information, and supports concurrent decoding of a whole-frame single decoder or N-fragment multi-decoders; and the frame synchronization and VCXO dynamic adjustment module outputs a latest complete frame in a whole frame mode, aligns and recombines fragmentation data according to a frame number in an N fragmentation mode, and dynamically adjusts a VCXO clock frequency based on a decoding and display time difference. According to the invention, multi-format compatibility, seamless switching of video streams and ultra-low delay display are supported, and black fields, static frames and picture tearing are effectively avoided.

The invention discloses a self-adaptive video compression coding method and system, and the method comprises the steps: monitoring the type of a current automatic driving scene in real time based on perception information, and obtaining a target coding parameter matched with the type of the current automatic driving scene according to the dynamically determined type of the current automatic driving scene; and performing video compression coding on image data acquired in real time under the corresponding automatic driving scene type by using the target coding parameter matched with the current automatic driving scene type. According to the method provided by the invention, the target coding parameters can be flexibly and adaptively adjusted in combination with different scenes, so that the adaptive target coding parameters can be adaptively selected under different automatic driving scene types to carry out video compression coding on the image data under the corresponding scenes, and therefore, better image quality can be maintained, and the video coding efficiency can be improved. Storage occupation can be compressed as much as possible, and comprehensive balance of image quality and storage size of different scenes is realized.

The invention discloses a bidirectional adaptive video super-resolution method based on frame difficulty index, and belongs to the field of computer vision. The invention provides a frame-level dynamic reconstruction method for solving the problems that simple frame calculation is redundant and difficult frame reconstruction is insufficient due to the fact that an existing model adopts a fixed calculation strategy for video frames with different difficulties. According to the method, a motion detail decoupling propagation network is constructed, motion information is efficiently transmitted by utilizing a shallow forward propagation branch, and a deep backward propagation branch focuses on recovering texture details so as to decouple a time sequence propagation task; meanwhile, a frame reconstruction difficulty evaluation network is introduced to generate a global difficulty index, so that the receptive field weight of the adaptive time sequence fusion network and the refining depth of the dynamic refining network are regulated and controlled. According to the method, through explicit modeling frame-level reconstruction difficulty, adaptive matching of the model capacity and the video frame feature complexity is realized, and the video reconstruction performance is remarkably improved under limited computing power.

The invention discloses a GB / T28181 monitoring video stream efficient transmission and decoding method based on HTTP3 (Hyper Text Transfer Protocol 3) and electronic equipment. According to the method, the problems of high delay and high resource consumption of a traditional GB / T 28181 video monitoring Web end playing scheme based on WebSocket are solved by utilizing a new generation Web API (Application Program Interface) of WebTransport and WebCodec. According to the invention, through the UDP transmission capability of WebTransport based on HTTP / 3, the direct transmission of the RTP data packet from the server to the Web client is realized, and protocol conversion does not need to be carried out; the WebCodes is adopted to realize efficient hardware acceleration decoding of the client; and meanwhile, a multiplexing real-time monitoring and self-adaptive video quality control mechanism is provided. Compared with the traditional scheme, the method has the advantages that the end-to-end delay is remarkably reduced, the resource consumption of the server is reduced, the concurrency capability is improved, the video parameters can be dynamically adjusted according to the network condition, and the method is suitable for video monitoring application in various network environments.

The invention discloses a space-time adaptive video lane line detection method based on differential memory and wavelet guidance, and mainly solves the problems of difficulty in collaborative modeling of space-time characteristics, easy loss of shallow geometric details and insufficient utilization of deep time sequence information in a dynamic scene in the prior art. According to the implementation scheme, the method comprises the following steps: acquiring marked video sequence data from a public video lane line detection data set, preprocessing the marked video sequence data, and dividing the preprocessed video sequence data into a training set and a test set; constructing a video lane line detection network comprising a feature extraction unit, a differential memory time sequence alignment unit, a wavelet-guided direction perception feature enhancement unit, a space-time adaptive fusion unit and a feature decoding unit; iteratively training the video lane line detection network through back propagation by using the training set; and inputting the test set into the trained video lane line detection network, and outputting a lane line. According to the method, the detection precision and robustness in challenging environments such as shielding, strong light and motion blur are remarkably improved, and the method can be used for realizing accurate extraction and stable tracking of lane lines in a dynamic driving scene.

The utility model discloses a self-adaptive video arm support adjusting structure which comprises a base, a main arm with one end rotationally connected with the base, an auxiliary arm telescopically connected in the main arm and a photographing piece installed on the auxiliary arm, and the photographing piece comprises an installation plate fixedly installed at the first end, away from the main arm, of the auxiliary arm. A mounting groove is formed in the mounting plate, and a first camera and a second camera are movably connected into the mounting groove. The angle of the arm body can be controlled through hinged rotation of the main arm on the base, and the length of the photographing piece connected to one end of the auxiliary arm can be adjusted through telescopic movable connection of the auxiliary arm in the main arm, so that the optimal photographing height and angle in a dredging detection area are formed. And meanwhile, the shooting height can be adjusted in real time according to the change of an actual scene, and the first camera and the second camera are movably connected on the auxiliary arm, so that an optimal shooting angle is formed in a working state.

Disclosed herein are innovations for bitstreams having clean random access (CRA) pictures and / or other types of random access point (RAP) pictures. New type definitions and strategic constraints on types of RAP pictures can simplify mapping of units of elementary video stream data to a container format. Such innovations can help improve the ability for video coding systems to more flexibly perform adaptive video delivery, production editing, commercial insertion, and the like.

The invention discloses a self-adaptive video transmission control method based on unmanned aerial vehicle bandwidth perception. The method comprises the steps that S1, an unmanned aerial vehicle video system sends a bandwidth perception data frame to a ground receiving end; s2, the ground end returns a bandwidth state data frame, wherein the bandwidth state data frame comprises a received signal strength indication (RSSI) and a frame loss rate (FLR) index; s3, the unmanned aerial vehicle video system analyzes the bandwidth state data frame and evaluates the channel quality grade; s4, the unmanned aerial vehicle video system calculates a buffer proportion factor of the annular buffer area; s5, the unmanned aerial vehicle video system calls a pre-constructed adaptive transmission control model, transmission parameters are updated according to the channel quality grade and the buffer proportion factor, and the transmission parameters comprise transmission video quality and coding parameters; s6, transmitting video data according to the updated transmission parameters of the unmanned aerial vehicle video system; and S7, the ground end receives the video data and decodes and plays the video data. According to the invention, the problem of poor adaptability in different network environments when the unmanned aerial vehicle sends the video data with the fixed channel parameters is solved, and the video transmission quality is improved.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system preprocesses raw data at edge computing devices, compresses the data into latent space vectors using distributed encoders within a variational autoencoder spanning edge and cloud components, decompresses the vectors using decoders, and processes them through a resource-aware neural upsampler to generate enhanced reconstructed outputs. The system dynamically adapts compression based on available computing resources and network conditions, while enabling secure distributed processing through homomorphic operations on compressed data. Edge-cloud coordination layers manage data flow, compression parameters, and workload distribution, while maintaining system reliability through intelligent failover handling and resource optimization.

A video coder may receive video data and apply a filter to a plurality of types of samples of the video data. The plurality of types of samples may include two or more of: an input of a fixed filter, an output of a fixed filter, an input of a signaled filter, an output of a signaled filter, an input of an adaptive loop filter, an output of an adaptive loop filter, an input of a sample adaptive offset (SAO) filter, the method comprises the following steps: receiving the input of an SAO filter, the output of an SAO filter, the input of a bilateral filter, the output of the bilateral filter, the input of a cross component SAO filter, the output of the cross component SAO filter, the input of a deblocking filter, the output of the deblocking filter, filtered reconstructed residual data, a dequantization coefficient, a filtered dequantization coefficient, a predictor or a filtered predictor.

The invention provides a perception adaptive video stream code rate decision-making method and system based on a neural symbol reinforcement learning buffer area, and belongs to the technical field of computer network and multimedia transmission. According to the method, a neural symbol reinforcement learning agent is constructed, a comprehensive state vector is generated by collecting a playing state, video content information and user preference, and double decisions of code rate selection and buffer area adjustment are jointly output; security rules are formalized in a first-order logic mode, security exploration is achieved through a security monitor and a security layer strategy, and illegal risks such as underflow of a buffer area are avoided; the bandwidth prediction precision is improved by adopting HTTP / 3 stable connection sampling and a hybrid prediction model; and designing a multi-target reward function to balance user experience quality and buffer occupation optimization. According to the method, high-video-quality, low-lagging and low-delay transmission can be stably realized in a low-speed to 5G high-speed network, and unnecessary buffer accumulation can be remarkably reduced on the premise of ensuring smooth playing, so that end-to-end delay is reduced.

The application provides a fountain code-based adaptive video transmission method, an electronic device and a storage medium, and the method comprises the following steps: receiving historical packet loss rates of all data packets corresponding to video image sent frames which are counted and fed back by a receiving end of a video transmission system; updating coding redundancy for fountain code encoding of a current frame of a video image based on the historical packet loss rates of the video image sent frames; compressively encoding the current frame of the video image to be transmitted to generate a compressed code stream data frame; fountain code encoding the compressed code stream data frame according to the coding redundancy to generate coding data packets corresponding to the current frame of the video image; and transmitting the coding data packets corresponding to the current frame of the video image to the receiving end of the video transmission system. The application can realize real-time video transmission with low computational complexity, low performance consumption and high decoding success rate.

Disclosed herein are innovations for bitstreams having clean random access (CRA) pictures and / or other types of random access point (RAP) pictures. New type definitions and strategic constraints on types of RAP pictures can simplify mapping of units of elementary video stream data to a container format. Such innovations can help improve the ability for video coding systems to more flexibly perform adaptive video delivery, production editing, commercial insertion, and the like.

The application relates to a kind of adaptive video streaming method and system based on serverless computing, belong to streaming media transmission technical field.System is realized by fine-grained serverless pipeline Video delivery, use stateless function to strengthen the response to video request event, use a kind of near-end strategy optimization PPO of three-end clipping based on deep reinforcement learning algorithm to solve the bit rate adaptive sequence decision problem in video playing process.In addition, dynamic video block quality factor is included in user experience QoE index, to configure QoE model, for each video block is assigned a priority weight, improves the robustness of video bit rate decision, thereby reduces video streaming delay, improves user viewing experience.

The adaptive video encoding method for live broadcast scenarios of the present invention reduces the resolution and bit rate by splitting the image into four channels of video at half the resolution, while simultaneously stitching the four channels back together to create a high-resolution, high-quality video, thereby adapting to different network bandwidths and devices. Compared to traditional methods, the present invention is equivalent to encoding only one channel of high-resolution video, which is simply split to achieve both low and high resolutions. While the coding efficiency, network bandwidth usage, and storage usage are equivalent to that of a single channel of high-resolution video in the traditional method, different resolutions and bit rates are achieved to meet the needs of different devices and network bandwidth conditions.

Disclosed herein are innovations for bitstreams having clean random access (CRA) pictures and / or other types of random access point (RAP) pictures. New type definitions and strategic constraints on types of RAP pictures can simplify mapping of units of elementary video stream data to a container format. Such innovations can help improve the ability for video coding systems to more flexibly perform adaptive video delivery, production editing, commercial insertion, and the like.