217 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 discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention belongs to the technical field of video communication processing, and particularly relates to an image quality improvement method for a video call of a fusion scheduling system, which comprises the following steps: performing quality evaluation on collected video frames, determining image quality information according to a quality evaluation value, a preset threshold value and statistical historical video frame quality, and sending the image quality information to a server; and then the code rate and the resolution used by the current video coding are obtained, and the video coding parameters are dynamically adjusted based on the image quality information, the code rate and the resolution, so that the self-adaptive video coding parameter adjustment based on the video content is realized, and the optimal video quality and transmission efficiency are achieved. According to the invention, the video data in the video call of the converged communication scheduling system is processed, and the problems that the image quality is reduced and the jamming is obvious are eliminated, so that high-definition and smooth video image quality is provided, the user experience is improved, and the use requirements are met.

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 present application relates to the technical field of communications, and discloses an adaptive video transmission method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a wireless channel environment parameter and a wireless scheduling parameter of a current period; inputting the wireless channel environment parameter and the wireless scheduling parameter of a preset target time sequence into a converged QoE prediction neural network model, and predicting a QoE measurement index for video transmission within a future preset duration; and on the basis of the predicted QoE measurement index, performing optimization adjustment on the wireless scheduling parameter of the next period to make the QoE measurement index of the next period reach a preset QoE standard for transmission.

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.

The invention relates to the field of network communication, and particularly discloses a network self-adaptive video conference transmission optimization method and system, and the method comprises the steps: systematically analyzing a historical data sequence of a network state, and extracting a deep trend and mode of a network behavior from the historical data sequence; meanwhile, different requirements of the type of the current conference video frame on transmission parameters (especially code rates) are fully considered, and the key information is fused into a decision-making process. Through a specially designed fusion model, the historical evolution trend of the network state and the specific requirement of the current video content can be intelligently subjected to interactive perception and cooperative processing, so that a video target code rate which can adapt to future network fluctuation and can meet the current picture quality requirement is generated. Based on the dynamically optimized target code rate, the system further determines an appropriate target resolution and an appropriate target frame rate, and the video is coded and transmitted.

Techniques for serving a manifest file of an adaptive streaming video include receiving a request for the manifest file from a user device. The video is encoded at different reference bitrates and each encoded reference bitrate is divided into segments to generate video segment files. The manifest file includes an ordered list of universal resource locators (URLs) that reference a set of video segment files encoded at a particular reference bitrate. A source manifest file that indicates the set of video segment files is identified based on the request. An issued manifest file that includes a first URL and a second URL is generated based on the source manifest file. The first URL references a first domain and the second URL references a second domain that is different from the first domain. The issued manifest file is transmitted to the user device as a response to the request.

The invention discloses a QP self-adaptive compressed video super-resolution method based on compression prior. The method mainly comprises the following steps that a feature extraction block is used for extracting features of a compressed video and the compression prior of the compressed video; constructing a QP modulation module, and recalibrating features by using a frame level QP; constructing a self-adaptive multi-scale prior information fusion module to form a prior fusion module, and fully utilizing coding prior and video features in a video stream; a bidirectional propagation alignment module is used to extract spatial and temporal features and align the fused features; constructing an adaptive enhancement modulation module to form a reconstruction module, and adaptively enhancing features; the feature extraction block, the prior fusion module, the bidirectional propagation alignment module and the reconstruction module jointly form a QP adaptive network, and the network can realize super-resolution for different QP compressed videos. According to the QP self-adaptive compressed video super-resolution method based on compression prior, good subjective and objective effects can be obtained.

The invention relates to the technical field of video coding, in particular to a self-adaptive video coding method and device, equipment and a medium. Obtaining a video frame sequence, and carrying out segmentation operation on the video frame sequence; predicting the coding unit set according to a preset prediction mode; performing binary coding calculation according to the prediction error, a preset quantization parameter and a preset probability interval; evaluating according to the link state information and the historical network flow data to obtain a network broadband state; calculating according to the network bandwidth state and a preset gradient information evaluation formula to obtain a frame rate and a resolution; performing adaptive adjustment on the coding unit set according to the frame rate, entropy coding and resolution to obtain video coding; in a segmentation stage, coding units are divided according to video contents, an intra-frame mode is optimized in a prediction link, parameters are reasonably set during coding, a frame rate and a resolution ratio are dynamically adjusted through network adaptation, finally, codes are adaptively generated, bandwidth is efficiently utilized, and network changes are flexibly adapted.

The invention discloses an AI reasoning adaptive video push-pull stream speed adjusting method, and relates to the technical field of video data management. Comprising the steps that 1, key parameters of video stream transmission are collected regularly, state data of video streams are monitored in real time, the state data comprise the current frame rate, the video cache queue length and the reasoning queue state, and data analysis is carried out according to the key parameters and the state data to predict possible network bottlenecks; 3, dynamically calculating the optimal speed of the push-and-pull flow based on the length of a video cache queue, the state of the reasoning queue and the change of the display time of a single picture, and calculating the optimal speed of the push-and-pull flow according to the current system load, the network condition, the user experience and the predicted network bottleneck. And step 4, obtaining adjusted feedback information, setting a threshold value, and dynamically adjusting the push-pull stream speed so as to maintain synchronization between the video stream and the reasoning task and prevent overload of the reasoning queue.

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.

Various embodiments of the technology described herein relate to compression of video data, including selecting a pivot image from a video including a plurality of images and causing a first machine learning model to generate a descriptor of the pivot image, where the descriptor includes a language description associated with the pivot image. In one example, the pivot image and the descriptor are provided to a decoder for reconstruction of the video. In an embodiment, the decoder includes a generative machine learning model that takes as an input the pivot image and the descriptor. The decoder uses the pivot image to generate an image based at least in part on the descriptor. The image is combined with other images generated by the generative machine learning model to reconstruct the video.

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.

A video coder may receive video data, and apply a filter to a plurality of categories of samples of the video data. The plurality of categories of samples may include two or more of an input of a fixed filter, an output of the fixed filter, an input of a signaled filter, an output of the signaled filter, an input of an adaptive loop filter, an output of the adaptive loop filter, an input of a sample adaptive offset (SAO) filter, an output of the SAO filter, an input of a bilateral filter, an output of the bilateral filter, an input of a cross component SAO filter, an output of the cross component SAO filter, an input of a deblocking filter, an output of the deblocking filter, filtered reconstructed residual data, dequantized coefficients, filtered dequantized coefficients, a predictor, or a filtered predictor.

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.

A device may generate, based on a first input frame and a second input frame of an input video, a first feature map and a second feature map. The device may generate a forward optical flow map and a backward optical flow map based on the first feature map and the second feature map. The device may generate an error estimate for the interpolated frame using the forward optical flow map and backward optical flow map. The device may select, based on a comparison of the error estimate and one or more criteria, an interpolation module from a plurality of interpolation modules. The device may generate the interpolated frame using the selected interpolation module. The device may combine the interpolated frame with the two input frames to generate at least a part of an output video.

This document relates to distributed teleconferencing. Some implementations can employ adaptive audio or video enhancement to address scenarios where audio enhancement can tend to remove desirable sounds. For instance, adaptive audio enhancement can involve detecting the presence of a sound, such as clapping, and modifying audio enhancement so that the sound is retained in an enhanced audio signal. Adaptive video processing can involve detecting the presence of the sound and adding a graphical identifier to a video signal that conveys the presence of that sound.

The invention discloses a ship-shore video transmission system and method, and relates to the technical field of ship communication. The ship-shore video transmission system comprises a user side, a ship side and a shore side, the ship end comprises a client and a self-adaptive video coding module; the shore end comprises a server; the server is used for acquiring a playing instruction of a ship-end target camera and sending a transmission instruction of a current video stream of the ship-end target camera to the client; the client is used for receiving a transmission instruction of a current video stream of a ship end target camera and calling the self-adaptive video coding module; and the adaptive video coding module is used for adaptively coding the current video stream of the ship end target camera to obtain a target video stream, and transmitting the target video stream to the shore end and the user end. Therefore, by arranging the adaptive video coding module, adaptive coding adjustment can be performed on the current video stream of the ship-end target camera, so that delay can be reduced, and stability and reliability of ship-shore video transmission can be improved.

The invention provides a multi-user VR video streaming media rate-based adaptive adjustment method and system, and relates to the technical field of VR, comprising the following steps: constructing a double-layer architecture of an edge layer and a user layer, carrying out video space-time segmentation and multi-quality coding on the edge layer, and realizing video adaptive transmission based on user motion information; a forward and reverse weighted user experience evaluation model is adopted to carry out optimal allocation on system resources; a quality balanced user subset is constructed in combination with time dimension equilibrium constraint, and resource dynamic optimization is carried out; the multi-dimensional resource verification model is used to realize video quality grade optimization distribution; and generating a panoramic frame through integrity verification, time sequence synchronization and spherical projection reconstruction. According to the invention, the service quality balance of multi-user VR video transmission is improved, the system resource overhead is reduced, and the user watching experience is enhanced.

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 discloses an adaptive video stream optimization and transmission method and system, and the method comprises the steps: segmenting a video stream into fragments, and dynamically adjusting the fragment duration according to the proportion of a dynamic scene in the fragments; the method comprises the following steps: pre-analyzing a scene complexity label in head metadata of a fragment before decoding of a client, and dynamically selecting a decoding resolution and a frame rate of the fragment in combination with a current equipment screen refresh rate and environment illumination intensity sensor data; and in the transmission process, if it is detected that the buffer area occupancy rate of the client side is lower than a dynamic first threshold value based on the product of the network round-trip time and the fragment average code rate, a pseudo fragment filling mechanism is triggered. According to the invention, through scene intelligent perception, dynamic resource allocation and end-to-end cooperative control, the limitation of single-point optimization of a traditional scheme is broken through, and the problems caused by complexity difference of video contents and dependence of buffer management on a fixed threshold are solved.

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.

Systems and methods for language augmented video editing are disclosed. A method includes presenting a video editing assistant (e.g., via a communicatively coupled display and / or speaker). The method includes, in response to receiving a request from a user to create adaptive video content that satisfies a set of characteristics identified based on the request i) analyzing, using a first machine-learning model, existing video content to identify portions of the existing video content that satisfy the set of characteristics and ii) for each portion of the existing video content that satisfies the set of characteristics, create adaptive video content using a respective portion of the existing video content that satisfies the set of characteristics. The method includes generating, using a second machine-learning model, descriptions of the adaptive video content and presenting the adaptive video content and the descriptions of the adaptive video content.