862 results about "Video quality" patented technology

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 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.

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 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 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.

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 relates to the technical field of video coding, in particular to an intelligent video coding optimization system and method based on computer vision, and the system comprises a computer vision video content analysis module; identifying an object, a behavior and a face in the video; the video classification module based on the neural network is in communication connection with the computer video content analysis module; based on the video feature data, classifying videos by using a deep neural network; the video coding parameter optimization module is in communication connection with the video classification module based on the neural network; adaptively optimizing video coding parameters based on the video classification result and the video feature data; the video coding quality evaluation module is in communication connection with the video coding parameter optimization module; evaluating the video quality of coding based on the optimized coding parameters; and the evaluation result is fed back to the video coding parameter optimization module for further optimizing the coding parameter, so that the understanding depth of the video content is greatly improved.

The invention provides a method for generating a dynamic three-dimensional Gaussian scene based on an image of a diffusion model. The method comprises the following steps: S1, generating a static three-dimensional Gaussian scene; s2, generating a dynamic video: generating the dynamic video according to the input image by using a stable video diffusion model; s3, optimizing video quality: processing the dynamic video frame by frame by using a diffusion model, and obtaining an optimized dynamic video image through back propagation; and S4, generating a dynamic three-dimensional Gaussian scene: combining a six-way feature plane (Hexplane) and three-dimensional Gaussian splashing as dynamic scene representation, extracting features from the dynamic scene representation, and introducing a multi-layer perceptron decoder to regress displacement, rotation and zooming information of three-dimensional Gaussian, thereby obtaining the dynamic three-dimensional Gaussian scene. According to the method, the diffusion model and the three-dimensional Gaussian splash are combined for use, the diffusion model is used for strengthening the time correlation and the detail quality, and the problem of low rendering speed is solved based on the three-dimensional Gaussian splash by using the explicit characteristic and the advantages of high efficiency and micro rendering.

The invention belongs to the technical field of video quality evaluation, and particularly relates to a video quality evaluation method based on spatio-temporal feature fusion. Comprising the following steps: acquiring a video quality evaluation data set, preprocessing the video quality evaluation data set, and processing preprocessed video data by adopting a time feature extraction module to obtain short video features and long video features; processing the preprocessed video data by adopting a spatial feature extraction module to obtain high-level semantic features and distortion features; fusing the short video features, the long video features, the high-level semantic features and the distortion features by using a feature fusion module to obtain fused features; inputting the fusion feature into a multi-layer perceptron for processing to obtain a video quality score; calculating the total loss of the model and continuously adjusting model parameters according to the total loss of the model to obtain a trained video quality evaluation model; performing video quality evaluation by using the trained model; according to the method, the correlation between the spatial-temporal features and the inter-frame features is effectively utilized, so that the accuracy of quality evaluation is improved.

The invention provides a video conference method, system and device and a storage medium, and relates to the technical field of network communication, the method comprises the following steps: obtaining network performance information, user behavior information, video quality information and retrieval service use information, the retrieval service use information comprising a retrieval service calling state; taking the network performance information, the user behavior information and the video quality information as input, and obtaining a predicted code rate through a pre-constructed first parameter adjustment model; judging whether the retrieval service is called or not according to the calling state of the retrieval service, and if not, adjusting the video parameters according to the predicted code rate; and if yes, obtaining a preset resolution and a preset frame rate, calculating a video code rate to obtain a target code rate, and adjusting the video parameters according to the target code rate. According to the method, the optimal video code rate is predicted in the conference process through the pre-trained first parameter adjustment model, and the video data transmission parameters are adjusted according to the retrieval behavior of the user, so that the information acquisition efficiency of the user is improved.

Tensor-Product B-splines (TPB) have been shown to improve video quality when used to represent reshaping functions to map reshaped standard dynamic range content into high dynamic range (HDR) content; however, TPB prediction is computationally intensive and may not be supported by legacy devices. Methods and systems for backwards-compatible signaling of TPB-related metadata and a fast TPB prediction method are presented to overcome both of these limitations. Computation overhead for a TPB-based 3D look-up table is reduced by using temporary two-dimensional arrays. A remapping of the most significant bits of a legacy bit-depth parameter allows for backwards compatibility.

A no-reference video assessment framework employs a multi-resolution input representation and a patch sampling mechanism on a video having multiple frames 302 to aggregate information across different granularities in spatial and temporal dimensions. The framework effectively models complex spacetime distortions that occur in user generated content-type videos. According to one aspect, the framework embeds video clips 302 as multi-resolution patch tokens using complementary modules. This includes a multi-resolution video embedding module 322, and a space-time factorized Transformer encoding module 324, 326. The multi-resolution video embedding module 322 is configured to encode multi-scale quality information in the video, capturing both global video composition from lower resolution frame and local details from larger resolution frames. The space-time factorized Transformer encoding module 324, 326 aggregates the spatial and temporal quality from the multi-scale embedding input, and is configured to output a quality score 330 for the input video.

The invention relates to the field of artificial intelligence, financial science and technology and digital medical treatment, and discloses an image-based video generation method and device, equipment and a storage medium. The method comprises the following steps: receiving and preprocessing an input static image to generate a multi-scale feature; based on the multi-scale features, determining an optimal space-time processing path through differentiable search of a space-time architecture generator, and generating output features fused with time sequence dynamic information; generating a video frame based on the time sequence recurrent neural network and the output features; and inputting the video frames into the video frame sequence, and generating a target video through the video frame sequence. According to the method, the optimal space-time processing path can be automatically determined through differential search, manual intervention is avoided, the time sequence coherence and detail authenticity of the video are ensured by dynamically generating the output characteristics of the fusion time sequence information and generating the video, the video generation quality and efficiency are improved, and the video quality is improved. The method is suitable for high-precision video generation such as video synthesis, content creation and virtual reality in the fields of finance and medical treatment.

The invention relates to the technical field of streaming media, in particular to an energy-saving video adaptive bit rate optimization method based on deep reinforcement learning, which introduces a video quality evaluation method of video multi-method fusion evaluation in the optimization process, effectively evaluates the perception quality of a video at different bit rates, and improves the video quality. It is ensured that the user obtains the most real picture experience in the watching process; meanwhile, an energy consumption perception model is provided by combining a neural network of an Actor-Critic architecture, and the energy consumption condition of equipment in the video playing process is estimated based on the energy consumption of video data downloading and video rendering; besides, by improving an entropy updating strategy of a near-end strategy optimization algorithm, multi-dimensional information such as network conditions and user preferences is combined more effectively, the bit rate decision of the video stream is optimized, and the video quality and the energy consumption are balanced. According to the invention, on the premise that the user experience quality is ensured, the equipment energy consumption is obviously reduced, so that the dual optimization of the video stream transmission quality and the energy consumption is realized.

The invention discloses an immersive video quality evaluation method and device based on viewpoint space-time correlation, and relates to the technical field of video image processing, and the method comprises the steps: obtaining an immersive video containing a multi-viewpoint texture video and a multi-viewpoint depth video, and extracting a texture video block, a depth video block, a texture key frame and a depth key frame from the immersive video; inputting the extracted data into the trained immersive video quality evaluation model for processing; the model comprises a texture depth feature space-time interaction part, a texture video quality evaluation part and a depth video quality evaluation part; and obtaining a texture video score and a depth video score through model interaction processing, and carrying out weighted aggregation on the scores to obtain a final immersive video quality score. According to the method, the multi-view texture and depth information in the immersive video is acquired and processed, so that the quality of the immersive video is evaluated.

Systems and methods are disclosed related to a 3D grounded video foundation model. A video generation method and system provide 3D conditioning information to a video diffusion model to improve generated video quality (object and temporal consistency) that is grounded in three dimensions (3D). The video generation method and system also enable precise camera control, cinematic effects, and scene editing. Video output corresponding to a set of camera specifications is generated for a scene from input image(s) including one or more images of a static scene or a sequence of images (video) for a dynamic scene. The input image(s) are used to calculate a 3D cache representing the scene. The 3D cache is rendered according to the set of camera specifications to produce a frame sequence and a mask sequence that identifies missing pixels in each frame. The frame sequence is encoded and masked to generate the output video.

The invention relates to a video generation method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. According to the method, for video description information input by a user, a plurality of split scripts can be generated, a plurality of split pictures are generated after the user confirms the split scripts, and a final video is generated after the user confirms the split pictures, and the video can be automatically generated only by inputting the video description information by the user, so that the video generation efficiency is improved; moreover, in the process of generating the video, the generated split script and the split picture are displayed, and the video is generated after the user confirms, so that the participation degree of the user is improved, namely, the controllability of the user on the generated video is improved, the video generation efficiency is improved, the generated video can better meet the requirements of the user, and the user experience is improved. And the quality of the generated video is improved.

The invention relates to the technical field of traffic management, in particular to a road scene multi-target tracking system based on visual perception of an unmanned aerial vehicle, which realizes efficient acquisition and low-delay return of road scene videos through a high-definition camera and a 5G communication technology, and provides a reliable data basis for subsequent processing. Through data preprocessing and flight control, the video quality and the flight efficiency are improved, and the path of the unmanned aerial vehicle is dynamically adjusted according to the motion trail analysis result of the key dynamic target; a key dynamic target is obtained through a one-shot detector model and an optical flow method, and effective tracking is realized in combination with a deep learning algorithm and a multi-target tracking technology, so that the accuracy of target detection and the stability of tracking are improved; the motion trail of the key dynamic target is visually displayed by analyzing the motion trail of the key dynamic target and visualizing the analysis result of the motion trail of the key dynamic target.

The invention discloses a method for improving the quality of a space-time domain compressed video by using a dense network, and the method mainly comprises the following steps: firstly, inputting a low-quality video obtained by HEVC compression into a network, aligning a certain frame to be enhanced with three adjacent frames before and after the frame in a universal deformable convolution mode, and carrying out the reconstruction of the frame to be enhanced; extracting an initial space-time fusion feature; extracting spatial feature information containing more levels from a to-be-enhanced frame by using the dense structure; the initial space-time fusion features and the spatial features are input into a space-time information adaptive fusion module together, and optimized space-time information is obtained through a series of introduced attention mechanisms and convolution layers; and finally, sending to a reconstruction module, obtaining an optimal residual error through a network, and adding the optimal residual error with a compressed frame to obtain a reconstructed video frame. Experimental results show that the method can effectively suppress the compression effect of the video, improve the video quality and obtain a better visual effect.

The invention discloses a video quality evaluation method with rich information representation, which comprises the steps of spatial domain feature extraction, motion feature extraction, time sequence modeling and quality prediction, and is characterized in that a fused feature vector is input into a time domain convolutional network for time sequence modeling, and the feature dimension is reduced to 128; and finally, directly mapping the 128-dimensional features into the quality score of the video through a full connection layer. According to the method, spatial domain feature extraction, motion feature extraction, time sequence modeling and quality prediction are set, the spatial domain feature extraction part of the model considers different features represented by video frames in RGB and YIQ color spaces so as to fully extract the features of the video frames in the spatial domain, and in addition, in order to fully understand the relation between adjacent frames, the feature extraction part of the model considers the different features represented by the video frames in the RGB and YIQ color spaces. Motion information on a time sequence is supplemented by extracting motion features of a video, so that the model has better performance, the accuracy of model prediction is improved, the overall model has better performance and accuracy in video quality evaluation, and the overall effect of the model is improved.