1111 results about "Video quality" patented technology

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention relates to the technical field of traffic management, in particular to a video monitoring traffic intelligent optimization system which comprises a time sequence fragment evaluation module, a path priority adjustment module, a traffic scheduling optimization module and a buffer strategy adaptation module. According to the invention, the transmission efficiency and quality of the video monitoring data are improved by analyzing the video stream in real time and adjusting the frame rate and the compression rate, the importance of each time sequence segment in the video stream is evaluated, the frame rate is adjusted according to the importance, more resources are ensured to be obtained by important events, unnecessary data transmission is reduced, network congestion is effectively reduced, and the transmission efficiency of the video monitoring data is improved. The network transmission path is monitored in real time, and the data transmission proportion is adjusted, so that the video stream passes through the network path with higher quality more preferentially, the stability and speed of video transmission are enhanced, the priority and bandwidth allocation are dynamically adjusted, and the video stream with high priority is protected to ensure that the video quality of a key monitoring area is not influenced by network fluctuation.

The invention discloses a park security all-weather monitoring system and monitoring method based on a digital twinborn model, and relates to the technical field of security monitoring, the system obtains and stores video streams and environmental data in real time through a data acquisition module, and provides a basis for subsequent analysis. The central processing module carries out preprocessing and feature extraction on the data to generate a video image feature set and an environment feature set, and the digital twin monitoring module visualizes environment features through a three-dimensional model, so that visual display of dynamic changes of the chemical workshop is realized. And the video quality analysis module comprehensively calculates a comprehensive monitoring performance index Ezh by using the video quality index Q (t) and the monitoring regulation and control index C (t) so as to ensure the definition and the stability of video monitoring. And the comprehensive evaluation and early warning module combines the abnormal detection value A (t) with the comprehensive monitoring performance index Ezh, calculates a comprehensive safety risk index O (t) and generates early warning information to improve the safety management level of the park.

The invention provides a monitoring video transmission method and system based on wireless communication, and relates to the technical field of monitoring video transmission, and the method comprises the steps: obtaining and preprocessing network state data, the network state data comprising bandwidth, time delay and packet loss rate; establishing a network state prediction model based on the LSTM neural network, and predicting a future network state according to the network state prediction model; based on the future network state, a multi-objective optimization algorithm is adopted to perform transmission path selection; and for the selected transmission path, video coding parameters are adjusted, the coding parameters comprise a code rate, a resolution ratio and a frame rate, and a self-adaptive transmission protocol is adopted to dynamically adjust the transmission rate according to a real-time network state. According to the method, different paths can be dynamically selected, network congestion or degradation is avoided, and the video transmission quality and rate are adaptively and flexibly adjusted according to the current network state, so that stable transmission of the video quality under different network conditions is ensured.

The invention provides a multi-path embedded video coding method and system based on generative artificial intelligence. The multi-path embedded video coding method comprises the following steps: firstly, coding input cue words and demand parameters, and outputting cue word vectors and demand parameter code streams; then decoding the cue word vector and the demand parameter code stream, and outputting a feature vector and a feature map; extracting the feature vector, the feature map and the representation guide vector to obtain multi-embedded feature information; reconstructing a picture by using the obtained multi-embedded feature information through a generative AI and obtaining a video to be iterated; performing video quality evaluation on the video to be iterated; and generating a binary code stream for transmission through the current cue word vector, the demand parameter code stream and the multi-embedded feature information input. According to the method, important information characteristics are transmitted in a highly abstracted manner, and a high-definition video is reconstructed at a receiving end, so that the storage space required by video coding and the size of a transmission information stream are greatly reduced, and higher-definition transmission under a narrower bandwidth is realized or video coding under an extremely low bandwidth becomes possible.

The invention relates to the technical field of data processing, in particular to a video monitoring data processing system based on cloud computing, which comprises a video quality synchronization module, an abnormal behavior identification module, a resource scheduling optimization module and a safety early warning trigger module, and synchronizes a video stream timestamp through a cloud parallel processing technology based on multi-source video data. According to the invention, the video stream timestamp and the image quality are precisely synchronized through the cloud parallel processing technology, the high quality of the data stream is ensured, the screening mechanism ensures that only the video stream meeting the quality standard is further analyzed, and the accuracy of subsequent abnormal behavior analysis is enhanced. The system not only can analyze and quickly identify abnormal behaviors deviating from a normal track in real time, but also can dynamically adjust, calculate and store resources according to actual resource use conditions, so that the processing speed and the cost efficiency are improved, the system can flexibly meet monitoring requirements of different scales, and the system is particularly suitable for a dynamic environment and has wide application prospects. And the early warning and response capabilities of video monitoring are improved.

The invention relates to the technical field of video compression, in particular to a video monitoring data compression and transmission method and system, and the method comprises the following steps: analyzing the texture features of a video frame, calculating the gray gradient of each pixel, obtaining the texture change rate, detecting edge information, calculating the distribution density of edge pixels, and extracting a color histogram. And calculating the dispersion degree of color distribution. In the invention, a dynamic coding parameter adjustment mechanism based on content is introduced to analyze the video frame, a compression strategy is adjusted according to the content complexity, and the coding process can accurately meet the requirements of different video contents through comprehensive analysis on the texture change rate, the edge density and the color dispersion degree; according to the scheme, the coding parameters can be dynamically adjusted by monitoring the coded video quality in real time and comparing the coded video quality with the preset quality threshold value, and the storage and transmission of data are further optimized while the video quality is ensured.

The invention belongs to the technical field of video processing, and particularly relates to a monitoring video enhancement method for a farm, which aims to solve the technical problem of low quality of an enhanced video in the prior art, and comprises the following steps: S1, processing each frame of image in a monitoring video sequence frame by frame; s2, distinguishing a target animal area from a background area, and identifying and generating an artifact mask; s3, aiming at the background area, carrying out key smoothing processing on the artifact position to inhibit the artifact; s4, for the image of the target animal area, performing adaptive nonlinear enhancement on the brightness component, and performing color correction on the chrominance component; s5, performing pixel-level fusion on the enhanced target animal area and the background area; and S6, spreading the information of the previous frame to the current frame by using the forward optical flow field, and carrying out weighted fusion on the information of the previous frame and the current frame. According to the method, the target bred animals, the background areas and the artifacts are accurately distinguished, so that refined and differentiated processing of pictures is realized.

The invention relates to the technical field of video optimization, and discloses a video content adaptive optimization system based on deep learning, which comprises a video extraction module, a video decoding module, a coding adjustment module, a video backtracking module and a video evaluation module, the video extraction module is configured to obtain video feature data according to the motion complexity feature vector, the scene switching feature vector and the texture feature vector, and the video decoding module is configured to decode the video feature data based on an initial deep learning model to obtain video adjustment parameters. The coding adjustment module is configured to obtain a first output video, the video backtracking module is configured to obtain a target deep learning model and obtain a second output video based on a first output video adjustment parameter, and the video evaluation module is configured to judge whether the video quality of the second output video reaches the standard or not; and if not, obtaining a third output video according to the historical input video adjustment parameters. The method provided by the invention has the characteristics of good adaptability and optimization for different input videos.

The invention belongs to the technical field of biological medicine, and particularly relates to a video content quality analysis and knowledge recommendation method and system based on a large model, and the method comprises the steps: carrying out the video obtaining through an input keyword based on a short video platform, and obtaining a video ID, a video content text and video metadata; based on the video content text, multi-dimensional text information extraction and problem diagnosis are carried out, and a structured video text is generated; performing multi-dimensional scoring on the structured video text, calculating to obtain a video quality score, and constructing an initial high-quality video candidate set; based on the initial high-quality video candidate set and the user input question, calculating a similarity score of the user input question and the initial high-quality video candidate set, and constructing a high-quality video candidate set; and in combination with the video quality score and the similarity score, carrying out weighted calculation on a comprehensive recommendation score of each video in the high-quality video candidate set, and outputting the first three recommended videos and recommendation reasons to realize precise recommendation.

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.

Provided are a video generation method, an electronic device, and a computer readable storage medium, relating to the technical fields of computers and video processing. The video generation method comprises: acquiring a target text, wherein the target text is used for describing video content to be generated (S21); and using a target video generation model to perform video generation processing on the target text to obtain a target video, wherein the target video generation model is a model obtained by performing model alignment on an initial video generation model and a preset reward model in a fine-tuning manner (S22). The present invention solves the technical problems in the related art that a video generated by a video generation model obtained by training based on network data has poor quality and does not meet user expectations.

The invention provides a panoramic video three-dimensional point cloud sparse reconstruction method and device integrated with key frame screening, and the method comprises the steps: obtaining a panoramic video of a target scene, and carrying out the video quality evaluation of the panoramic video, and obtaining a video evaluation result; determining a target resolution of downsampling according to the video evaluation result; carrying out downsampling processing on the panoramic video based on the target resolution to obtain a downsampled second video; for the second video, dynamically adjusting a frame extraction interval based on a preset key frame selection mechanism, and extracting a frame index of the key frame from the second video according to the frame extraction interval to obtain a plurality of frame indexes; and obtaining a plurality of corresponding key frames in the panoramic video based on the plurality of frame indexes, and obtaining the three-dimensional scene sparse point cloud of the target scene based on the plurality of key frames, thereby solving the technical problem of low processing efficiency in the prior art.

The invention relates to the technical field of network transmission, in particular to a video data transmission method, which comprises the following steps of: based on a video transmission data packet, analyzing a sending time point of the video data packet, recording a sending moment and a serial number, checking a time stamp at a plurality of transmission nodes, and generating a time stamp data packet. According to the invention, by matching the timestamps and the serial numbers, marking the data packets and checking the transmission nodes, the security and reliability of data transmission are improved, errors and loss of the data packets in the transmission process are reduced, and the video quality is optimized by analyzing the video data, identifying abnormal image frames and adjusting transmission parameters. Compression parameters and server resource allocation are dynamically adjusted according to video contents and in combination with real-time user requirements, so that video quality is maintained under the condition that the bandwidth is limited, the size of a buffer area and the capacity of a data packet are adjusted according to network conditions, and the fluency and continuity of video playing are improved.

The invention discloses a short video editing synthesis method based on fuzzy logic. The method comprises the following steps: S1, carrying out digital video preprocessing on an input original short video; s2, forming a short video feature data set; s3, generating a fuzzy logic decision result; s4, performing multi-target and multi-parameter optimization on the fuzzy logic decision result by adopting a random score programming method to form an optimized editing parameter set; s5, generating a preliminarily synthesized short video; s6, generating a video quality evaluation result; and S7, carrying out iterative optimization on the preliminarily synthesized short video according to a video quality evaluation result, adjusting the editing parameters and re-executing the editing synthesis step until the video quality evaluation result meets a preset standard, and generating a final edited finished product. According to the method, the editing scheme can achieve the best balance in the aspects of visual rhythm, information integrity and emotion expression consistency.

The invention discloses a multi-angle video automatic editing method based on content defect detection, and relates to the technical field of video processing. According to the method, multi-track video materials are accurately synchronized to the same time axis through cross-correlation calculation, and flaws in video content are analyzed frame by frame and classified and identified by applying fuzzy detection of a dynamic threshold and combining jitter detection and shielding detection of rotation and displacement estimation. And according to a detection result, an editing scheme is intelligently generated through a dynamic programming algorithm of state transition optimization, high-quality fragments are selected, defective parts are automatically removed, the continuity and multi-view diversity of video contents are reserved, and finally, a high-quality recorded video is generated through integration. The video editing efficiency is greatly improved, the manual intervention requirement is reduced, meanwhile, it is ensured that the quality of the output video meets the professional application requirement, and the method is suitable for multiple scenes such as conference recording, educational training and multi-angle video production and has high practical value and popularization prospects.

The invention discloses a video compression intelligent preprocessing method, system and device based on frequency domain perception optimization and a medium. The method comprises the following steps: constructing a preprocessing model; preprocessing the input coding frame and the reference frame; enabling the preprocessed coding frame and reference frame to pass through a virtual encoder to obtain a frequency domain coefficient of a residual frame, and calculating a code rate of the coding frame; enabling the frequency domain coefficient of the residual frame to pass through a virtual decoder to obtain a decoded frame; training a preprocessing model; testing the effect; the system, the equipment and the medium are used for implementing the method. According to the method, a frequency domain sensing module is introduced into a virtual encoder in a training stage, modeling is carried out on a frequency domain information context, and statistical redundancy between transformation units is eliminated; an optical flow collaboration module is introduced into a virtual encoder, local distortion of a prediction frame is repaired in a feature domain by using high-level semantic information, and the difference between training and actual deployment is effectively reduced; through the optimization, the preprocessing model can better learn the rate distortion characteristic of the standard encoder, and the quality of the preprocessed video is remarkably improved.

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

The invention provides a video quality detection method based on multi-scene self-adaption. The method comprises the following steps: selecting an area in a video picture, extracting multi-dimensional features of the area, determining the type of a scene where the video picture is located, and dynamically adjusting video detection parameters of the video picture; detecting the quality of the video picture based on the video detection parameters; intercepting an abnormal video picture, and adding the abnormal video picture into the constructed equipment fault template library M; and performing local model retraining on the video quality detection model based on the equipment fault template library M, and detecting the quality of a to-be-detected video picture based on the video quality detection model. On the basis of deep analysis of different scene features, the scene type of the video picture is accurately judged through multi-dimensional feature calculation, and then the video quality detection parameters are dynamically adjusted according to the scene type, so that the detection system can automatically adapt to the optimal detection parameters according to scene changes, misjudgment and missed judgment caused by the scene changes are avoided, and the detection efficiency is improved. And the accuracy of video quality detection in different scenes is remarkably improved.

The invention relates to the technical field of content recognition, in particular to an Al-based video content analysis method and system, and the method comprises the following steps: carrying out the image segmentation learning based on a video sequence through employing a U-Net convolutional neural network, analyzing scenes and elements in a video frame, recognizing and isolating key visual elements in a video through network learning, and carrying out the recognition of the key visual elements in the video. Comprising objects and figures. According to the method, image segmentation learning is carried out by adopting the U-Net convolutional neural network, key visual elements in the video can be identified and isolated more accurately, a clearer basis is provided for follow-up scene change and key event tracking, video content analysis is carried out by applying the graph neural network, the identification capability of dynamic scenes and events is enhanced, and the identification efficiency is improved. Deeper structured understanding is provided for video content indexing, the video quality is analyzed through a structural similarity index evaluation method, and the reason of visual quality reduction can be accurately recognized and improved.

The invention discloses an AI-based highway video quality diagnosis method, system and device and a medium, and relates to the technical field of video quality diagnos.The method comprises the steps that an initial diagnosis parameter set is determined based on obtained monitoring device parameters, environment condition information and quality standard information, and video quality problem points are recognized; analyzing different scene complexity regions of the preprocessed target video stream by using the initial diagnosis parameter set, diagnosing video quality problem points, and obtaining image instruction evaluation indexes and optimization suggestion parameters of each region and the problem points; learning and optimizing a preset imaging characteristic model and a common problem mode library on the basis of the collected historical video samples, determining a target video quality improvement scheme and dynamic adaptive adjustment parameters in combination with image quality evaluation indexes of different scene complexity regions, and optimizing and repairing the video quality; according to the invention, the quality of expressway videos is improved, and the performance and reliability of a video monitoring system are improved.

The invention discloses a CDN-based video downlink weak network adaptive image quality optimization method and system, and relates to the technical field of video content distribution. The method comprises the following steps: S1, generating a basic compensation amount according to frame interval deviation and frame content complexity; s2, calculating a rendering overhead index and a rendering urgency threshold according to the state of the multi-source equipment, and generating an interpolation correction coefficient for frame rendering time sequence adjustment; s3, constructing a channel damage quantization map segment trigger redundancy control and introducing an equipment load state constraint; and S4, basic waiting time is generated according to local historical features, and a final waiting time value is generated in combination with the playing buffer area state and the composite load factor. Through cooperation of frame time sequence compensation enhancement, rendering scheduling optimization and segmentation redundancy control strategies, improvement of video image quality stability and end side load adaptability in a weak network environment is realized.

The invention relates to the technical field of video monitoring communication, in particular to a 5G high-definition video monitoring method and system for a smart city, and a medium, and the method specifically comprises the steps: obtaining a 5G high-definition video based on the moving features of each feature point of a moving target between each frame and a previous frame image in a monitoring video, and the gray gradient change condition in each channel image of an RGB image of each video frame; constructing an information rich characteristic value of each video frame; calculating a bandwidth influence coefficient of each detection time period based on a data fluctuation abnormal condition of the network bandwidth data in each preset detection time period; constructing a video transmission quality coefficient by combining the features, optimizing a quantization parameter of a video coding algorithm in the current detection time period, carrying out coding transmission on the monitoring video in the current detection time period, and carrying out intelligent analysis on the transmitted monitoring video; the problem of frame loss or low video quality of video content with relatively high potential information value is reduced; and the interference on subsequent video monitoring intelligent analysis and target detection can be reduced.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention relates to a crowd-sourced geographic video data quality evaluation and three-dimensional reconstruction method, device and equipment. The method comprises the following steps: acquiring a to-be-processed video frame sequence; the to-be-processed video frame sequence is obtained by preprocessing public source geographic video data; constructing a video quality evaluation network; the video quality evaluation network comprises a pre-trained dynamic object detection module, a lens conversion detection module and a scene detection module; and processing the to-be-processed video frame sequence by using the video quality evaluation network, splicing the continuous scene frames output by the shot conversion detection module and the shot transition frames marked by the scene detection module according to timestamps to obtain a qualified video frame sequence, and performing three-dimensional reconstruction according to the qualified video frame sequence. By adopting the method, the quality and availability of the public-source geographic video data can be improved, and the precision and efficiency of three-dimensional reconstruction are improved.

The invention discloses a method, a system and equipment for generating an oral video of a digital human, and a medium, and the method comprises the steps: obtaining oral copywriting and video material data, and analyzing and determining a timestamp of the copywriting in a video material through a multi-mode large model; converting the copywriting into audio data, preprocessing the audio data, and combining the audio data with the timestamp to generate first video data; and generating a digital human according to a user demand, and combining the digital human with the first video after image matting processing to obtain an oral playing video. According to the method, a traditional template generation mode is broken through, and customized production of the digital human broadcast video is realized through a multi-modal semantic matching and personalized digital human generation technology; and meanwhile, audio and video accurate synchronization, high-quality image matting and synthesis technologies are adopted, so that the content adaptability and the video quality are guaranteed, and the flexibility, the efficiency and the effect of digital population broadcast video production are remarkably improved.

The invention provides a video subtitle extraction method, a video subtitle extraction device and electronic equipment, after image data and audio data are extracted from an original video file, in order to accurately extract diversified subtitles from a video image, multi-scale subtitle image feature extraction is performed on the image data, and compared with traditional OCR (Optical Character Recognition) identification, the image subtitle extraction efficiency is improved. Meanwhile, voice recognition is performed on the audio data in the original video file, and the obtained voice recognition result is not influenced by the image quality, so that the defect of the subtitle recognition result extracted from the image data when the video image quality is poor can be overcome, and the accuracy of the subtitle recognition result is improved. Therefore, the caption file generated after the caption recognition result is corrected based on the voice recognition result can reliably meet the auxiliary requirements of audiences on video understanding or other processing requirements on the video captions.

A system may include one or more devices configured to register a camera device with a core network; assign the camera device to a subscription of a user equipment (UE) device registered with the core network; and assign the camera device to an uplink streaming network slice. The one or more devices may be further configured to assign an uplink streaming video Quality of Service (QOS) class to a data flow sent from the camera device to the core network, wherein the uplink streaming video QoS class guarantees at least one video streaming QoS parameter; and establish the data flow from the camera device to a destination device via the uplink streaming network slice based on the uplink streaming video QoS class.