3065 results about "Frame rate" patented technology

The invention provides a real-time streaming media transmission method and system based on a cloud game and a storage medium, and the method comprises the steps: monitoring network state parameters in real time, and generating network state data; dynamically adjusting the resolution, the frame rate and the bit rate of the video stream based on the network state; a redundant data packet is inserted, and the redundancy proportion is adjusted according to the packet loss rate and the jitter, so that the data reliability is enhanced; dynamically adjusting the transmission rate by adopting a congestion control algorithm; dynamically selecting an optimal server node according to a network condition and a server load; audio and video processing tasks are shared through edge computing nodes, and the load and transmission delay of a main server are reduced; setting a self-adaptive decoding buffer area, dynamically adjusting the size of the buffer area according to a network state, and reducing playing lagging; through real-time monitoring, dynamic adjustment and edge collaboration, smoothness is guaranteed, reliability is improved, delay is reduced, efficient load distribution and user experience optimization are achieved, and the method is suitable for real-time streaming media transmission in a cloud game scene.

The invention provides a textile blending proportion defect detection method, which comprises the following steps: carrying out continuous image acquisition on a blended textile in a vibration state, and adjusting the exposure time and frame rate of camera equipment according to vibration frequency and amplitude characteristics to obtain a textile surface image sequence; the obtained textile surface image sequence is preprocessed, and warp and weft areas are segmented according to the surface texture features of the blended textile; based on the abnormal degree evaluation model, constructing a blending proportion flaw classifier, and training the classifier to identify different types of blending proportion flaws by using the warp and weft abnormal features, the fiber distribution features and the texture features as input; according to a blending proportion defect classifier, in combination with defect positioning requirements in a vibration environment, performing real-time tracking on detected different types of defect proportion areas, compensating image displacement caused by vibration, and ensuring that a defect detection result corresponds to an actual textile position.

The invention discloses an automatic labeling and acquisition standardization method and system based on video content, and the method comprises the steps: decoding a video file into a processable video frame sequence, carrying out the standardization processing, unifying the format and the coding standard, and adjusting the resolution and the frame rate; performing content analysis on the standardized video frame sequence, extracting key frames, identifying objects, scenes and actions, and generating a video analysis result and a preliminary label; according to a video content analysis result, separating an audio stream from the video to carry out noise reduction processing and voice-to-text analysis, and combining the processed text with the video content analysis result to generate multi-modal comprehensive annotation information; based on the comprehensive labeling information, taking feedback of the user in video retrieval, label clicking and watching behaviors as an adjustment basis of label weights, and updating weight distribution of labels by analyzing user behavior data; labeling information and weight distribution are integrated into an index database, and indexes are updated in real time according to user behaviors and content changes.

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 an improved YOLOv11-based high-reflection and high-transmittance material surface defect detection method, which comprises the following steps: S1, acquiring a mobile phone screen surface defect image, preprocessing and labeling, and generating a training data set containing three defects of scratches, edge breakage and cracks; s2, constructing a defect detection model; s3, averagely dividing an input image into four independent detection regions, adopting a bounding box overlapping area weight distribution strategy for cross-region defects, and performing partition counting on the image by using a defect detection model; s4, constructing a surface defect detection network meeting the real-time requirement of the industrial production line, and outputting a detection result and a processing frame rate; s5, pixel-level mask segmentation is carried out on the scratch defect, and contour coordinates and geometric features are extracted; and S6, fitting the actual damage size of the scratch defect on the surface of the high-reflection and high-transmittance material according to the segmentation result. According to the method, the detection speed is high under the condition that the accuracy is ensured, and a reliable solution is provided for surface flaw detection of the high-reflection and high-transmittance material.

The invention relates to the technical field of visual rendering, and provides a three-dimensional data visual rendering method and a visual rendering engine system.The method comprises the steps that space segmentation processing is conducted on three-dimensional model data to be rendered through the LOD technology to generate block slice data with different geometric detail levels, organizing the block slice data through a tree structure with a spatial relationship, and outputting the tree structure as an index file; the method comprises the following steps: adaptively screening block and slice data of different levels of detail based on a user vision field range, storing the block and slice data into a rendering request queue, querying block and slice data to be rendered from the rendering request queue according to index codes of the block and slice data, and updating a layer browser database; according to the method, to-be-rendered block slice data are downloaded and analyzed in parallel in batches in a separated collaborative operation mode, and a rendering task is executed according to a data analysis result, so that the rendering scheduling frame rate is remarkably improved, and rapid visualization of urban mass three-dimensional slice data at a webpage end is realized.

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 game cartoon character display method based on a virtual reality technology. The method comprises the following steps: acquiring three-dimensional model data of a game cartoon character in a target display area through a user interaction terminal; the virtual reality content management platform determines a dynamic rendering precision level according to the model data, and generates a real-time rendering strategy including a model patch reduction coefficient, a texture compression rate and a skeleton animation updating frequency in combination with terminal performance parameters; the strategy is sent to a virtual reality supervision platform and a user interaction terminal, and a virtual reality rendering engine platform is instructed to execute real-time rendering; and the virtual reality supervision platform monitors the frame rate fluctuation data of the head-mounted display device, calculates the scene rendering stability, sends a rendering optimization instruction if the scene rendering stability is lower than a threshold value, and adjusts the model data acquisition frequency and the video memory cleaning period to optimize the performance. The rendering efficiency and the system stability can be improved, and the hardware load and the frame rate fluctuation are reduced.

Systems and methods are described for video coding using generalized bi-prediction. In an exemplary embodiment, to code a current block of a video in a bitstream, a first reference block is selected from a first reference picture and a second reference block is selected from a second reference picture. Each reference block is associated with a weight, where the weight may be an arbitrary weight ranging, e.g., between 0 and 1. The current block is predicted using a weighted sum of the reference blocks. The weights may be selected from among a plurality of candidate weights. Candidate weights may be signaled in the bitstream or may be derived implicitly based on a template. Candidate weights may be pruned to avoid out-of-range or substantially duplicate candidate weights. Generalized bi-prediction may additionally be used in frame rate up conversion.

The invention discloses a global ultra-precision strain measurement method, and relates to the technical field of strain measurement. The method comprises the following steps: continuously shooting moving images of a target area by using a high-frame-rate high-definition camera, and recording accurate time; and carrying out image correction, feature extraction and tracking and data normalization. And after Gaussian white noise is added, singular value decomposition and reconstruction are carried out. And a Lucas-Kanade optical flow method is adopted to calculate the displacement of the feature points in the reconstruction matrix. And a displacement smoothing stage: smoothing the displacement data by using an LOESS method. And carrying out global strain calculation by adopting a window sliding method, and calculating the strain through the spatial derivative of the displacement field. According to the method, the quantization effect can be relieved, the continuity of displacement calculation is improved, low-energy noise and irrelevant features are removed, main motion information is reserved, meanwhile, smoothness is enhanced, local features are captured in a high-precision mode, and measurement sensitivity is improved. The method is suitable for strain measurement of large-scale structures such as bridges and buildings.

The invention discloses an intelligent track foreign matter detection system and method based on image recognition, and particularly relates to the technical field of image recognition. A real-time image is acquired, an area-of-interest is extracted in combination with an image segmentation algorithm, and potential foreign matters are identified; the space-time consistency of the detection result is verified by analyzing the space-time data of the track foreign matter so as to judge the real threat; extracting multi-dimensional features such as shape, texture, color and size from a real threat target, and outputting classification confidence to evaluate model classification stability; the detection accuracy is evaluated by combining the space-time consistency and the classification stability, the detection results are divided into different categories, the abnormal degree of subsequent detection is predicted for the incomplete accuracy detection result, and the image acquisition resolution or frame rate is dynamically adjusted to improve the detection definition. The problems of missing detection and frequent false alarm of small and medium-sized foreign matters in the prior art are solved, the accuracy and reliability of track foreign matter detection are greatly improved, and the running safety of a high-speed train is guaranteed.

The invention discloses a container application on-screen method based on texture full-process optimization, which comprises the following steps: taking a swan-gap system as a host and an Android system as a container, distributing a plurality of first buffers by the host, screening out a first texture format by the container, writing original textures of the format generated by rendering into frame buffers, identifying a dirty area of the rendered textures, and displaying the original textures in the frame buffers; packaging the texture data of the region, storing the packaged texture data into a shared memory, transmitting a file descriptor FD to a host, binding an idle first buffer setting priority, a state, a generation timestamp and a texture data pointer at the same time, and obtaining the texture data by the host through the FD; the host establishes a fixed thread task pool, executes multiple tasks in parallel to obtain on-screen texture data, and then updates the state of the first buffer and a texture data pointer; and finally, the host queries the first buffer with the processed state, reads the texture data after sorting according to the priorities and the generation timestamps to finish on-screen, and resets the state of the first buffer to be idle, so that the on-screen delay is effectively reduced, and the actual frame rate of the Android application is improved.

The invention relates to an edge fusion method and system of an XR immersion type LED display screen. The method comprises the following steps: carrying out feature extraction on image data of an XR immersive LED display screen to obtain an RGB three-channel spatial domain feature matrix and a frequency domain edge feature matrix; performing feature processing to obtain a multi-core context feature map; performing coding processing to obtain a position-brightness comprehensive coding feature matrix; performing multi-scale decomposition to obtain a multi-scale detail feature map; carrying out pixel clustering on the edge region of the XR immersive LED display screen to obtain an edge topological structure diagram and a brightness transition function; and performing dynamic matching on the display frame rate through a PID controller, and performing brightness compensation and color correction on the edge region to obtain edge fusion data. The method has the multi-scale feature extraction capability, the edge fusion problem under different scenes can be adaptively processed, and it is ensured that processing delay meets the real-time display requirement, so that the overall performance of an XR immersive display system is improved.

The invention relates to an image frame insertion method and device in virtual shooting and a storage medium. The method comprises the following steps: acquiring at least two frames of reference images generated by a rendering engine; obtaining motion information and depth information of at least two frames of reference images generated by a rendering engine; and based on the motion information and the depth information of the at least two frames of reference images, determining a pixel value of a frame insertion image between the at least two frames of reference images, the frame insertion image and the at least two frames of reference images being used for sending to a display screen for display, and the display screen serving as a shooting background in virtual shooting. According to the embodiment of the invention, on-screen content can be smoothly played without improving the performance of a rendering end, the picture quality of a picture is remarkably improved, the data output capability of a rendering engine is fully played, redundant calculation is avoided, the computing power is saved, the remarkable frame rate improvement can be realized at relatively low cost, and the user experience is improved. The method meets the requirements of a virtual shooting scene, and has good stability and robustness.

Various solutions for adaptation of measurement gap configuration with dynamic control signaling are described. A network node may determine a traffic type. The network node may determine a measurement gap repetition period for the traffic type according to a periodicity of a reference signal (RS) and a video frame rate associated with the traffic type. The network node may transmit a measurement gap configuration with the measurement gap repetition period to a user equipment (UE).

The invention discloses a non-contact heart rate detection method, system and device based on visual Transform and multi-scale feature aggregation and a medium. The method comprises the steps that a face visible light video is obtained, the unified video frame rate is re-sampled through the frame rate, face key point positioning and region division are carried out on each frame of image, and a face visible light video sequence is obtained; performing time migration operation on the video sequence to obtain a difference frame sequence, performing channel fusion on the video sequence and a difference frame, and performing down-sampling on a spatial dimension to obtain a low-resolution feature tensor; constructing a non-contact heart rate extraction model, inputting the low-resolution feature tensor into the non-contact heart rate extraction model, and outputting a heart rate value of the target; the non-contact heart rate extraction model comprises a feature enhancement module, a multi-scale mask feature aggregation module, a Transform time sequence modeling module and an rPPG predictor, the system, the device and the medium are used for achieving the non-contact heart rate detection method based on visual Transform and multi-scale feature aggregation, and the precision of rPPG signal extraction is improved.

The invention relates to the technical field of digital fusion, in particular to an electronic chart drawing method and system based on a data fusion technology, and the method comprises the steps: S10, building and dynamically updating a mixed index structure based on a preset electronic chart database and an incremental data packet collected in real time; s20, matching the same-name elements, fusing the identified space overlapping regions, and outputting a de-duplicated global sea chart data set; s30, dividing a plurality of sub-regions, constructing a quadtree-R tree index, and outputting a quadtree-R tree double-layer mixed index structure; s40, converting the screen coordinates into geographic coordinates, screening visible elements, and outputting a visible region element set; and S50, based on the visible area element set and a preset style rule, allocating a rendering task, optimizing and matching a GPU instruction set, and outputting a real-time sea chart picture with a preset frame rate. According to the method, the real-time processing efficiency and the visualization fluency of the electronic chart are remarkably improved, and the user interaction experience is optimized.

The invention discloses a visual precise plastic mold surface milling system and method, and the method comprises the steps: obtaining and analyzing the surface topography data of a milling region and a tool displacement track in real time through an optical sensor, and extracting wear-related micro-deformation features through multi-band filtering processing; generating a deviation vector matrix through three-dimensional mapping of the deviation vector matrix and a reference curved surface, and realizing dynamic calculation and path correction of the cutter offset; the high efficiency and stability of the compensation cutting process are ensured by combining the self-adaptive adjustment of the rotating speed and the feeding rate of the main shaft; meanwhile, the compensation effect is projected to a visual interface in real time to form a comparison layer, hardware acceleration is started to maintain the system response speed when the frame rate fluctuates, and finally the material removal volume is evaluated according to the deviation vector matrix to intelligently control the machining cycle termination time. Therefore, the problem of microtopography deviation caused by tool abrasion in the milling process of the complex curved surface mold is effectively solved, and the machining precision and efficiency as well as the robustness and the automation level of the system are remarkably improved.

The invention discloses a display method and system for a mini-computer host, and relates to the technical field of computer display, and the method comprises the steps: monitoring and analyzing the ambient light intensity and color temperature in real time through a built-in ambient light sensor of the mini-computer host, and collecting ambient light data; the display brightness and the color temperature are intelligently calculated and adjusted through a pre-trained deep learning model by using the ambient light data; when video stream coding parameters are dynamically adjusted, network conditions and video stream complexity are analyzed in real time, and resolution and frame rate are adaptively adjusted; and combining the optimized display parameters with the adjusted video stream and the display. Through ambient light intelligent perception, network path optimization and video stream parameter dynamic adjustment, the problems of complex illumination adaptability and smooth video playing in the prior art are solved, and the user experience is greatly enhanced.

In various examples, a trailer angle may be estimated using one or more machine learning models to predict one or more keypoints on the center axis of the trailer drawbar (e.g., a keypoint representing the drawbar junction around which the drawbar pivots, one or more other keypoints along the center axis), back-projecting the predicted keypoint(s) onto a three-dimensional (3D) representation of the ground, and calculating the angle between the longitudinal axis of the towing vehicle and a line or ray formed by or fitted to the projected keypoints. The trailer angle may be estimated at any frame rate. For each frame, keypoints may be predicted from that frame and / or optical flow or some other type of feature tracking may be used to propagate predicted keypoint(s) from a preceding frame in lieu of predicting keypoint(s), and the resulting keypoint(s) may be used to estimate the trailer angle for that frame.

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

The invention discloses a sports motion recognition method based on 3D space-time attention and a slowfast network, and the method comprises the steps: obtaining an original human body motion video, sampling the original human body motion video through different frame rates, generating Slow branch and Fast branch input images, and taking the Slow branch and Fast branch input images as training samples; constructing a dual-branch network based on spatio-temporal feature extraction; designing a loss function, and training the dual-branch network based on spatial-temporal feature extraction by using the training sample; and sampling a to-be-identified human body behavior video through different frame rates to obtain corresponding video frame images, and inputting the video frame images into the trained dual-branch network based on spatial-temporal feature extraction to obtain an identified human body behavior category. According to the sports action recognition method based on the 3D space-time attention and slowfast network, the problems that in the prior art, the locality of the dependency relationship between the features in the space-time dimension is captured, and the model complexity is high are solved.

The invention relates to the technical field of ruin three-dimensional construction, in particular to a ruin three-dimensional model construction method fusing SLAM point cloud and image features, which comprises the following steps: acquiring a depth image and RGB data, aligning and eliminating inconsistent data according to a timestamp, calculating a frame rate to node ratio, establishing a binding relationship between an image and a point cloud, and extracting three-dimensional coordinate points. Recognizing and classifying geometric structures, evaluating texture and edge response, adjusting resolution, analyzing static and dynamic structures, eliminating dynamic interference, calculating geometric curvature and fitting error, optimizing three-dimensional model display, and obtaining detail reduction degree scores. According to the method, through accurate synchronization of the point cloud data and the RGB image, strict verification of timestamp matching and data consistency is allowed, data errors and noise are significantly reduced, the accuracy and reliability of the model are ensured, structural elements are identified, dense areas of geometric structures are refined and analyzed, richer data level support is provided, and the method is suitable for large-scale popularization and application. And higher-quality data is provided for archaeological research.

The invention relates to a video frame data segmentation right confirmation and dynamic transaction method and system based on a block chain. The method comprises the following steps: segmenting video stream data according to a preset frame rate to generate independent frame images, and carrying out anti-collision hash calculation on the independent frame images to generate a unique identifier; generating a comprehensive value score through a multi-modal analysis model in combination with the independent frame image and the corresponding script text data; generating NFT assets anchored to the block chain according to the comprehensive value score and the unique identifier; and dynamically adjusting the income distribution proportion of the NFT assets through the smart contract based on the user film watching data. According to the method, uniqueness right confirmation of frame data is realized through precise segmentation and anti-collision hash coding, value evaluation is performed by using a multi-modal analysis model, and capitalization and on-chain right confirmation are completed by means of a block chain technology. In addition, the income distribution proportion is dynamically adjusted in combination with the smart contract, efficient transaction and right distribution of the video frame data are realized, and the commercial value and transaction efficiency of the video data are 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 relates to the technical field of data processing, and discloses an automatic visual large-screen display processing method based on a control model and data driving, which comprises the following steps: optimizing large-screen information display, and switching parameters through layout density, visual attention and scenes; dynamically adjusting a data display strategy, and calculating data freshness, abnormal fluctuation and correlation parameters; user interaction experience is improved, and hot area response and multi-dimensional drilling parameters are optimized; enhancing information readability, and calculating graphic cognition and color conflict parameters; the system performance is stabilized, and the rendering frame rate and the data throughput are monitored; the business value, the attention decision response and the abnormity discovery timeliness are measured; dynamic weight, abnormal fusing, self-learning and multi-dimensional evaluation systems are adopted in intelligent optimization. According to the method, dynamic weight distribution, an abnormal fusing mechanism, self-learning optimization and a multi-dimensional evaluation system are combined, an intelligent, real-time and precise large-screen display system is achieved, and information display reasonability, interaction experience and system stability are improved.

The invention discloses a three-dimensional model lightweight reconstruction and rendering method based on multi-strategy collaboration, and the method comprises the steps: 1, extracting geometric information, appearance information and semantic attribute information of a three-dimensional model, separating the geometric information from the appearance information and the semantic attribute information, and storing the semantic attribute information to a three-dimensional model database; 2, instantiating components with the same geometric structure in the three-dimensional model, realizing material sharing through a material reference mechanism, and reducing rendering overhead on the premise of not changing the structure of the model; step 3, carrying out lightweight processing on the exported geometric and appearance information to obtain reconstructed lightweight models with different precision grades; and 4, dynamically adjusting the geometric complexity of the lightweight model through the sight distance and the frame rate by using a hierarchical calculation algorithm for rendering, and meanwhile, obtaining component semantic attribute information and synthesizing the semantic attribute information by calling a three-dimensional model database interface to realize visual display of the reconstruction model.

The present invention relates to the field of video image processing technology, and specifically discloses a video image quality diagnosis and analysis detection system and detection method. By extracting images from an input video stream frame by frame and performing preprocessing, the key feature areas in the image are identified based on edge detection and color segmentation algorithms, and consistency features, frame rate stability features, and spatial distribution density features are extracted. By calculating and analyzing these features, a consistency feature index, a frame rate stability feature index, and a spatial distribution density feature index are generated. Finally, the image quality score of each key feature area is obtained by comprehensive processing. Through this process, the quality change of the video image can be accurately evaluated to ensure that it meets the preset quality standards. The present invention also has an adaptive adjustment capability, and can dynamically adjust the parameters of image quality detection according to real-time monitoring results, such as optimizing the frame rate calculation threshold and improving the feature extraction accuracy.

The invention discloses a reasoning resource allocation optimization method and system for multi-camera real-time video monitoring, and relates to the field of multi-camera real-time monitoring. According to the invention, through reasonable reasoning of resource allocation and combination of a video frame rate dynamic adjustment strategy, optimization processing of video streams of a plurality of cameras is realized. Particularly, in the method, different reasoning resources are allocated according to the priorities of the detection tasks of different cameras, so that the cameras with abnormal event detection requirements obtain more computing resources, and the cameras without abnormal events use fewer resources, thereby achieving the balance between the optimal performance and the computing resources.

A camera device may capture a high-resolution image of a frame and store the high-resolution image in memory. The camera device may down-sample the high-resolution image to a low-resolution image of the frame. The camera device may transmit, to an external compute node, the low-resolution image. The camera device may receive, from the external compute node, a request for a region of interest from the high-resolution image. The camera device may transmit, to the external compute node, the region of interest from the high-resolution image. The camera device may have a captured frame rate at which high-resolution images are captured. The camera device may also have an external frame rate based upon which all, or only some, of the high-resolution images may be down-sampled to low-resolution images that are transmitted to the external compute node. The external frame rate may be decoupled from the captured frame rate.