499 results about "Video quality" patented technology

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 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.

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 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.

The invention relates to the technical field of video monitoring, in particular to a livestock farm safety management method and system based on video monitoring, and the method comprises the steps: collecting a monitoring video of a livestock farm, and equally dividing each frame of video image into each CTU block; marking pixel points corresponding to the target object in each frame of video image as salient pixel points, and determining the static saliency of each CTU block; obtaining the motion pixel aggregation degree of each coordinate point in each frame of video image; determining the center-of-mass coordinate and the motion displacement of the target object in each frame of video image, and obtaining the dynamic saliency of each CTU block; and combining the static saliency and the dynamic saliency, determining a saliency weight of each CTU block, distributing a code rate for each CTU block, and carrying out coding compression on the video image for livestock farm safety management. Therefore, the monitoring video quality of the livestock farm is improved, and the safety management effect of the livestock farm is enhanced.

Techniques are described for training and use of machine learning models to determine objective video quality scores. Video quality scores predict the quality of video content perceived by viewers. Quality scores have various uses, including the selection of encoding profiles and determination of encoding ladders. A core model and residual model may be used to determine quality scores.

The invention relates to the technical field of industrial visual intelligence, in particular to an intelligent image selection and switching system fusing visual features and quality scores, which comprises the following steps: receiving multiple paths of video coding streams, inter-frame motion vectors and camera parameters; generating a macro block activeness distribution map based on the video coding stream and the inter-frame motion vector, and performing local window positioning and feature reconstruction on the video coding stream to generate an enhanced video vector; calculating a confidence coefficient mean value and a consistency score of the video coding stream according to the enhanced video vector, and fusing the confidence coefficient mean value and the consistency score with a channel transmission signal-to-noise ratio and a quantization noise increment to generate a video quality score; constructing a multi-criterion optimization model, and setting a feature representation vector for the multi-criterion optimization model; mapping the viewpoint weight based on the inner product of the feature representation vector, and calculating with the video quality score to generate a video switching score; and performing priority ranking based on the video switching score, and triggering a mapping switching instruction. And realizing video image scheduling by fusing the visual features and the quality score.

The invention discloses a screen content video quality evaluation method and device based on frequency-space complementation and semanteme, and relates to the field of computer vision, and the method comprises the steps: S1, extracting a video block and a key frame of a screen content video, and inputting the key frame into a high-frequency structure texture information extraction branch to obtain high-frequency structure texture information; s2, inputting the key frame into a noise sensing module to obtain a noise sensing feature; s3, inputting the noise perception features into a self-adaptive time sequence embedding module to obtain noise and semantic information; s4, splicing the high-frequency structure texture information and the noise and semantic information, performing quality regression to obtain a quality score of a single-frame key frame, and summing and averaging to obtain a spatial domain video quality score; s5, inputting the video blocks into a Fast-VQA-based quality evaluation branch to obtain a time sequence distortion perception degradation score; and S6, dynamically fusing the spatial domain video quality score and the time sequence distortion perception degradation score to obtain a final video quality score. According to the method provided by the invention, the screen content video quality is effectively evaluated.

The invention relates to a lightweight acoustic feature extraction method for end-side audio and video quality inspection. The method comprises the following steps: end-side equipment separates audio and video stream data through a double-time-sequence anchor point alignment method, and acquires independent audio data; performing grading preprocessing on the independent audio data to obtain noise-reduced independent audio data; on the basis of the segmented audio, a time domain and frequency domain collaborative extraction algorithm is adopted, multi-dimensional time domain features and frequency features sensitive to audio quality difference are obtained, and a lightweight acoustic feature vector is obtained through an incremental principal component analysis method; based on the lightweight acoustic feature vector, utilizing a dual-threshold matching judgment mode to obtain similarity between features; and on the basis of the feature similarity, through feature dimension deviation verification, obtaining a quality inspection result containing a standard grade and feature anomaly positioning. Lightweight acoustic feature extraction of end side audio and video quality inspection is realized.

A system comprises a video capture module to acquire a video, a preprocessing module to enhance video quality and isolate regions of interest within the video, and a biometric data extraction module using remote photoplethysmography to extract heartbeat and SpO2 levels from the video. A machine learning module analyzes the extracted biometric data for liveness and deepfake detection, a verification module to compare the analyzed data against known biometric signatures, and a user interface to display analysis results and alerts.

The invention belongs to the technical field of image communication, particularly relates to an AI-based video generation platform, and aims to solve the problems that in the use process of an existing AI-based video generation platform, generated videos are relatively single in originality, and the generated videos are insufficient in local detail fidelity and low in interaction efficiency, so that the quality of the generated videos is reduced. According to the scheme, the system comprises a data acquisition module, the data acquisition module is connected with a preprocessing module, the preprocessing module is connected with a fusion module, the fusion module is connected with a creative library module, and the creative library module is connected with a user-defined module; the video generation module is connected with the fusion module, the video generation module is connected with an editing optimization module, and through the technical means of the multi-modal data fusion module, the creative library module, the editing optimization module, interactive optimization and the like, the quality of the generated video can be effectively improved.

The invention relates to the technical field of video quality diagnosis, in particular to a video quality diagnosis method and system based on large model extensible classification, and the method comprises the steps: carrying out the sliding window sampling of an input video stream, recognizing and positioning an abnormal frame, and obtaining a plurality of image frames; respectively extracting visual features corresponding to the image frames and adding time codes and label codes; fusing time sequence information of historical frames through time coding and label coding to generate global time sequence characteristics; based on predefined abnormal category label text features, a label semantic vector set is generated and spliced, a query vector is generated through a full connection layer, an attention mechanism is utilized to match video features and label features, and a multi-label classification result is output; dynamically triggering an optimization process according to the abnormal confidence coefficient, and generating new tag text features through Few-shot sample input and low-rank adaptation fine tuning; and adding the new label text features into the label semantic vector set. The accuracy of a classification result with a small sample size is improved.

The invention relates to a video quality detection method and system, an electronic device and a storage medium, and is used for a reverse proxy server, the reverse proxy server is deployed at a cloud, and the method comprises the following steps: obtaining camera identification information reported by a proxy client; when a real-time streaming transmission protocol request containing a virtual real-time streaming transmission protocol port initiated by the PC client is received, forwarding the request to a corresponding proxy client according to camera identification information corresponding to the virtual real-time streaming transmission protocol port; the virtual real-time stream transmission protocol port establishes a mapping relationship with the camera identification information in advance; receiving video data uploaded by the proxy client; the video data is locally acquired by the proxy client from the camera according to the forwarding request; and encapsulating the video data into a standard real-time stream transmission protocol response, and returning the real-time stream transmission protocol response to the PC client for video quality detection. A communication link can be established for video transmission, the process is simplified, and the video quality detection efficiency is improved.

Innovations in machine learning (“ML”) models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

The invention relates to a video quality evaluation method and device based on artificial intelligence, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-evaluated video and a generated video text corresponding to the to-be-evaluated video, and enabling the generated video text to comprise a natural language used for describing at least one element in the to-be-evaluated video, the elements comprise scenes, actions, object change states and event sequences; performing structured analysis on the to-be-evaluated video through the visual language model to generate a video description text; and judging the semantic consistency of the generated video text and the video description text through a large language model, and generating a quality evaluation result of the to-be-evaluated video according to a judgment result.

Innovations in machine learning (“ML”) models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

The invention discloses a 4K multi-frame-rate YUV422 low-delay collaborative coding and decoding method, which relates to the technical field of video coding and decoding, and comprises the following steps of: configuring coding and decoding device parameters, establishing network connection, and setting a multi-frame-rate range and a buffer area; acquiring a 4K YUV422 original frame sequence from a video source and temporarily storing the 4K YUV422 original frame sequence; analyzing the difference and texture change between the current frame and the previous frame, and calculating the comprehensive content complexity; dynamically selecting an optimal target frame rate according to the complexity value and the network delay; the codec exchanges a frame rate switching command through a control channel and realizes synchronization, adjusts a compression parameter according to a selected frame rate, and generates a bit stream; and periodically exchanging network state information while transmitting the coded data. According to the invention, by introducing a dynamic frame rate collaborative adjustment mechanism based on content complexity, adaptive matching of coding parameters, scene contents and network states can be realized, and the problem that the stability of video image quality cannot be ensured while low delay is maintained in the prior art is solved.

Innovations in machine learning (“ML”) models used in adaptive post-processing of decoded video in a conferencing tool are described. For example, as part of post-processing of decoded video, a super-resolution / video restoration model increases spatial resolution (e.g., by interpolation between sample values), mitigates compression artifacts, and mitigates upscaling artifacts introduced when increasing spatial resolution. Or, as another example, as part of post-processing of decoded video, a video restoration model mitigates compression artifacts, without increasing spatial resolution. For adaptive post-processing, a post-processing model can be selectively applied depending on results of scenario detection, results of segmentation, and / or results of video quality analysis. With the innovations, a conferencing tool can in effect provide video at higher quality without significantly increasing the network bandwidth consumed by the video or, alternatively, provide video using less network bandwidth without significantly hurting the quality of the video.

The present application discloses a video processing method and apparatus, a device, a storage medium, and a computer program product. The method comprises: acquiring a video to be processed captured by an event camera and event data corresponding to the video to be processed; performing event feature extraction on the event data to obtain a motion region feature corresponding to the video to be processed; performing frame feature extraction on the video to be processed and the event data to obtain a motion holistic feature corresponding to the video to be processed, wherein the motion holistic feature is used for representing a dependency relationship between time and space of the video to be processed; performing feature fusion on the motion region feature and the motion holistic feature to obtain a fused feature; and performing video reconstruction on the basis of the fused feature to obtain a target video. The method provided in the present application can improve the resolution and frame rate of a restored video, thereby enhancing video quality.

A teleoperations system may be used to selectively override conditions detected by an autonomous vehicle to enable the autonomous vehicle to effectively ignore detected conditions that are identified as false positives by the teleoperations system. Furthermore, a teleoperations system may be used to generate commands that an autonomous vehicle validates prior to executing to confirm that the commands do not violate any vehicle constraints for the autonomous vehicle. Still further, an autonomous vehicle may be capable of dynamically varying the video quality of one or more camera feeds that are streamed to a teleoperations system over a bandwidth-constrained wireless network based upon a current context of the autonomous vehicle.

The invention discloses a multi-dimensional cross-modal video content structured arrangement system. The system comprises a video input interface, a voice recognition and semantic extraction module, a visual lens analysis module, a face recognition and attribute labeling module, a multi-modal information fusion and time alignment module and a structured output module. The beneficial effects of the invention are that deep fusion and accurate alignment of multi-dimensional cross-modal information are realized through the multi-modal information fusion and time alignment module, the problem of information isolation is overcome, strong association among voice, vision and figures is established, comprehensiveness of video content understanding is improved, and video quality is improved. And meanwhile, output is performed in a unified format through the structured output module, so that the convenience of downstream application is greatly improved, and the labor cost is remarkably reduced.

The invention relates to the technical field of information security, in particular to an AI video detection method based on multi-feature branch fusion, and the method comprises the following steps: extracting a frame extraction color image from a video, and obtaining a standardized frame sequence after preprocessing; calculating an inter-frame differential volume; obtaining a time sequence spectrum volume according to the standardized frame sequence; performing bilateral filtering decomposition on the standardized frame sequence to obtain an illumination consistency volume; respectively inputting the three types of volume features into a deep convolutional neural network, and after feature extraction and fusion, outputting an AI forgery probability through a classifier; and comparing the AI forgery probability with a decision threshold to generate a video category label, and calculating an index. According to the method, the multi-dimensional features of the time-space domain, the frequency domain and the physical illumination domain are fused, the potential traces of the deeply-forged video are effectively captured, the stable detection performance is kept under various video quality conditions, the accuracy is improved, and reliable technical guarantee is provided for media information security.

A system for monitoring video quality and performing actions in response to validated quality issues. Detected video quality loss is validated by deep learning models operating on various regions of a video scene. Patterns of related regions are provided to the deep learning models and their quality scores and artifact types evaluated. Combining the quality scores of interrelated regions allows for increased confidence that the quality loss is significant, and actions should be taken based on the cross-validated results.

The invention discloses a video quality evaluation method and system based on space-frequency combination and time sequence interaction, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the standardization and data enhancement of an immersive video key frame; then, spatial domain and frequency domain double-path features are constructed through learnable Gabor convolution, two-dimensional Fourier transform and a logarithmic magnitude spectrum; space-frequency dynamic fusion is realized through bidirectional Cross-Attention, and a joint representation is generated; then, inputting Transform architecture modeling inter-frame time sequence dynamic and global dependence, and obtaining time sequence correlation characteristics; and finally, outputting frame-by-frame quality scores through learning weighted MLP regression, and aggregating the frame-by-frame quality scores into an overall quality score. According to the method, high-fidelity no-reference quality evaluation of immersive video distortion is realized through space-frequency joint modeling and time sequence interactive learning.

The present application provides a video parameter adjustment method and apparatus. The method comprises: acquiring a first video segment, the first video segment comprising a digital person, and the digital person being generated by a neural network model; determining a video quality monitoring index on the basis of the first video segment, the video quality monitoring index comprising at least one of the following: the accuracy of the mouth shape of the digital person, the degree of synchronization between the picture and sound of the digital person, and the degree of consistency of the figure of the digital person; and determining a parameter of the neural network model on the basis of the video quality monitoring index, the parameter being used for generating the digital person in a second video segment. In the described method, a quality monitoring index related to a digital person in a livestreaming video can be monitored and corresponding feedback adjustment settings are executed, thereby improving the quality of the livestreaming video of the digital person.

Methods and systems are described to optimize a computation of a video quality metric. In one embodiment, a device receives a video clip, where the video clip includes a compressed video clip and an uncompressed video clip. The device may divide the video clip into a plurality of windows, wherein each of the windows includes a plurality of video frames. The device further may, for each of the plurality of windows, compute an estimated video quality error using a set of video frames selected from that window. Furthermore, if the estimated video quality error is greater than a threshold, the device may divide that window into a set of smaller windows and add the set of smaller windows to the plurality of windows, else the device may compute an estimated video quality score for that window.

The application provides a method and system for audio and video conference quality analysis and evaluation, relates to the technical fields of audio and video communication diagnosis and image processing diagnosis, and audio and video conference equipment health degree evaluation method, wherein the analysis method comprises: audio and video data collection: operation state monitoring and evaluation of audio and video conference core equipment; collecting key parameters affecting audio and video quality; establishing an evaluation model: converting collected data into quantifiable quality indicators, focusing on three dimensions of video lag, picture blur and audio break; identifying and verifying the voice: establishing a scoring prediction model: integrating equipment operation state data and three main dimension indicators to obtain the overall conference quality score and the reliability of the score, the application takes the severity of picture blur and other factors into account, the error of the static model is reduced, and the voice stability value is divided according to the region, so that the stability value can effectively reflect the overall audio quality in the conference room.

The invention relates to the technical field of image optimization, in particular to a video quality recovery method, device and equipment and a storage medium, and the method comprises the steps: carrying out the frame analysis of an original video, obtaining a video frame, and calculating the content complexity of the video frame; performing network depth adjustment on a target recovery model based on the content complexity to obtain a model after depth adjustment; wherein an encoder and a decoder in the target recovery model share the same weight set; processing the video frame based on the depth post-adjustment model to obtain a reconstructed frame; and obtaining a reconstructed video based on the reconstructed frame. According to the invention, the requirement on computing resources is reduced and the processing speed and flexibility are improved when the video quality is recovered.

The invention discloses a video transmission method and device based on Lyapunov optimization, electronic equipment and a storage medium. The method is applied to satellite node ends. The method comprises the following steps: acquiring initial state information of a current transmission time slot and video data to be transmitted; the initial state information comprises queue state information representing a coding environment and transmission state information representing a transmission environment; inputting the initial state information into a preset strategy action model to obtain action control parameters corresponding to the initial state information; and coding and transmitting the video data to be transmitted according to the action control parameters. Therefore, compared with the prior art that the video to be transmitted is coded and transmitted by adopting the preset fixed configuration parameters, the action control parameters output by the strategy action model dynamically adapt to the coding environment and the transmission environment of the satellite node end; the satellite node end encodes and transmits the video data to be transmitted according to the action control parameters, so that the video quality is better, and the transmission effect is better.