145 results about "Frame time" patented technology

The invention relates to a video generation method based on time sequence similarity, and is applied to the field of video generation. Specifically, the video generation method based on the time sequence similarity comprises the following steps: acquiring generated video information; generating a key video frame sequence with frame rate information based on the text information, wherein the key video sequence is separated by a plurality of frame marks; performing recursive interpolation processing on the key video frame sequence, fusing front and back key frame features through a preset attention mechanism, and generating an initial intermediate frame between adjacent key frames; extracting visual features of a plurality of key frames and intermediate frames included in the key video frame sequence, calculating cosine similarity of the visual features of adjacent frames, and adjusting intermediate frame generation parameters based on the similarity to optimize inter-frame coherence so as to obtain an optimized intermediate frame; and combining the key video frame sequence with the optimized middle frame to generate a complete video clip which is consistent with the text information and has a coherent inter-frame time sequence.

The invention discloses an AI-based creative thinking auxiliary generation method and system, relates to the technical field of artificial intelligence, and solves the problem of inaccurate sound and picture matching in a traditional method by performing timestamp alignment and feature extraction on audio data and a visual image frame and calculating the correlation between the audio data and the visual image frame by using a cross-modal attention mechanism. According to the method, user eye movement track data is introduced, real attention points of a user are mapped into a visual sequence, and an optimized weight matrix is generated by constructing attention masks and fusing model attention weights, so that a generation result is more in line with perception key points of the user. And meanwhile, a feedback mechanism is established based on the synchronization error score, and when the sound and the picture are detected to be asynchronous, the visual frame timestamp can be dynamically adjusted, so that the self-adaptive correction of the content is realized. On the whole, the method has remarkable advantages in the aspects of improving modal alignment precision, enhancing user perception consistency and optimizing generation result naturalness.

The invention relates to the technical field of video image processing, and discloses a video image super-resolution enhancement method based on a generative adversarial network. The method comprises the following steps: segmenting a low-resolution video sequence, and analyzing frame timestamp information to generate a dynamic time axis containing key frame nodes so as to reflect video time change characteristics; then, in combination with the time axis and an image feature extraction system, feature mapping is carried out on the low-resolution frames, and a preliminary high-resolution image sequence with feature information is obtained; enhancing the preliminary sequence by using a generative adversarial network containing a generator and a discriminator in combination with a context-aware optimization technology to generate an enhanced sequence; and a machine learning algorithm is introduced to identify and classify different image quality region characteristic modes, and an intelligent identification rule base is established, so that an enhanced sequence is optimized. And finally, in response to user interaction, adjusting display parameters to realize personalized display, integrating a multi-dimensional analysis tool, calculating details of a specific region, and presenting a result in a visual interface, so that the video image quality can be improved.

The embodiment of the invention provides a digital video monitoring data transmission synchronization method and system, and relates to the technical field of digital video monitoring data transmission synchronization, and the method comprises the steps: receiving a video data stream and an external sensor data stream which is related to the content of the video data stream and has an independent stable time synchronization mechanism; identifying a visual event from the video data stream, and identifying a physical event associated with the visual event from the external sensor data stream; taking the timestamp of the physical event as a time anchor point, obtaining a visual original timestamp of the visual event, and calculating the time deviation of the video data stream according to the time anchor point and the visual original timestamp; based on the time deviation, correcting the frame timestamp of the video data stream by adopting an adaptive weighted average or filtering strategy to obtain a corrected timestamp; and fusing the corrected timestamps to obtain a global time reference. The reliability of the monitoring data and the accuracy of the analysis result can be improved.

The invention relates to the technical field of video detection, in particular to an anomaly detection method and system fusing large and small models and cooperating with cross-time domain perception. Comprising the steps of configuring a small model, a multi-modal large model and a cross-time domain parameter based on an electric power scene; acquiring a monitoring video stream, calculating a dynamic frame extraction frequency based on a scene change coefficient of continuous video frames of the monitoring video stream, extracting a frame sequence and recording a timestamp of each frame; inputting a frame sequence into the small model to obtain a candidate target, removing redundancy through non-maximum suppression, screening an effective target in combination with a single-frame confidence coefficient and an inter-frame time domain continuous overlapping rate, marking an alarm image and generating a preliminary analysis result; a time-domain-crossing video frame, power scene sensor data and environment data are collected according to a dynamic time window by taking an alarm image timestamp as a center, a visual time-domain feature and a power scene sensor time sequence feature are extracted, and a time-domain-crossing fusion feature is obtained through fusion. The problem of misjudgment and missed judgment caused by single-frame dependence in power scene anomaly detection is solved.

A signal generator may include a reference horizontal synchronization signal generation block which generates reference horizontal synchronization signals based on a number of clock signals per a horizontal time, a frame clock calculation block which calculates a first frame clock number based on a number of the clock signals per the horizontal time, a frame clock comparation block which calculates a clock offset by comparing the first frame clock number and a second frame clock number generated based on a number of the clock signals per a frame time, a clock distribution block which generates horizontal synchronization signals by distributing a number of the clock signals corresponding to the clock offset to the reference horizontal synchronization signals, and a vertical synchronization signal generation block which generates a vertical synchronization signal based on the horizontal synchronization signals.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical science and technology, and discloses a video key frame extraction method, device and equipment and a medium. Then, a time-space relation graph of the video frames is constructed, nodes represent the video frames, and edge weights are jointly determined by inter-frame time proximity and visual feature similarity; performing feature propagation on the space-time relation graph by using a source domain pre-trained space-time graph neural network to obtain an initial importance score of each video frame; then, on the basis of scene characteristic indexes of a target video, through neural architecture search driven by a reinforcement learning strategy, topological structure parameters of the space-time diagram neural network are dynamically adjusted; and finally, recalculating the importance score of the video frame by using the adjusted space-time diagram neural network, and outputting a key frame sequence according to a scoring result, thereby improving the accuracy of key frame extraction and scene adaptability.

The invention relates to the technical field of audio and video synchronization, in particular to a video call real-time audio and video synchronous monitoring system which comprises a frame time extraction module, a main time sequence locking module, a delay structure analysis module, a synchronous node generation module and a synchronous control instruction module. According to the invention, through real-time extraction and analysis of timestamp difference calculation of audio and video frames and establishment of a transmission delay interval comparison sequence, the time difference in audio and video transmission can be effectively identified, the monitoring accuracy of an audio and video asynchronization phenomenon is enhanced, and through real-time adjustment of timestamp offset, the synchronization accuracy of the audio and video in a playing process is ensured; in a dynamic network environment, the offset rate is monitored and adjusted in real time, the continuity and fluency of interactive experience are ensured, the synchronization state of the audio and video frames can be controlled more accurately by analyzing the offset difference value centralized distribution section and marking the synchronization fluctuation section, and the situation that the large-scale user experience is reduced due to synchronization error accumulation is avoided.

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 application discloses a video instance segmentation method and system based on global feature enhancement, and belongs to the technical field of computer vision and deep learning, which comprises the following steps: inputting continuous video frames into a double-channel decoupling feature extraction module to obtain appearance and position features; inputting the two types of features into a tracker and a global feature memory updating module to dynamically update and obtain long-time sequence global context features; completing instance detection matching by the tracker, judging target state to obtain disappeared and newly appeared target queries; combining long-time sequence features to model current frame time sequence to generate enhanced features; finally, the tracker completes segmentation and tracking based on the enhanced features and the target queries, and outputs a segmentation mask sequence, an instance ID, a category ID and a tracking state. Through double-channel decoupling, double-memory block updating, multi-cost matching and multi-weight feature fusion, the application strengthens appearance feature representation and long-time sequence information utilization, and significantly improves segmentation accuracy and tracking robustness in complex scenes.

The invention discloses a one-way link data integrity rapid verification method and system, and relates to the technical field of communication security, and the method comprises the steps: determining a whistle frame interval and a fingerprint sampling layout in a window according to a preset rule, sending a whistle frame, and forming an effective anchor point at a receiving side according to tolerance verification; segmenting the to-be-verified data according to the block length, generating a block-level rapid verification value, and verifying according to arrival; extracting sparse fingerprints on the data stream according to the fingerprint sampling layout and sending the sparse fingerprints, and performing recalculation and comparison at corresponding positions; and judging that the window does not pass when the whistle frame intervals are accumulated in a border crossing manner or effective anchor points are insufficient, otherwise, establishing an anchor neighborhood by using the effective anchor points, counting expected sampling hit and dissociation, synthesizing whistle finger coupling steady quantity, and judging that the window passes when the block-level rapid verification is passed, the sparse fingerprints are consistent and the whistle finger coupling steady quantity is greater than a threshold value. Through combination of a whistle frame time sequence, block verification and fingerprint sampling in a time window, one-way link transmission consistency evaluation is realized, and the exception screening and tampering detection capabilities are improved.

The invention discloses a clock signal generation method, a module and a display device. The clock signal generation method comprises the following steps: within a frame time (F0), when an enable end (EN) provides an effective enable signal and clock indication ends (CPV1 and CPV2) provide effective clock indication signals, obtaining a time sequence for generating a clock signal; and when the generation of the clock signal is stopped, the timer (J1) performs timing, and the timing sequence for generating the clock signal again is obtained according to the timing time obtained by the timer (J1). Clock signal ends (CLK1, CLK2, CLK3, CLK4, CLK5 and CLK6) can be opened in any line according to needs, any line display of the display panel is achieved, and power consumption reduction and multi-scene application of the display panel are achieved.

The invention discloses a cross-device video synchronous display method, which relates to the technical field of video display, is used for solving the problems of video playing interruption and user experience reduction, and is used for detecting operation behavior data of a field terminal and quantifying the influence of the operation behavior data on the synchronization stability by synchronizing clock references at two ends and establishing a synchronous clock. Analyzing data generation characteristics, generating a drift index after detecting a drift distance of a synchronous clock, judging whether to trigger a transmission regulation and control mechanism by combining the data generation characteristics, and when entering the transmission regulation and control mechanism, acquiring network state data of a field terminal and receiving buffer data in a remote terminal; according to the method, data sending and reading time sequences of a field terminal and a remote terminal are adjusted, frame timestamps at two ends are detected after adjustment, whether a transmission regulation and control mechanism is triggered again or not is judged according to the frame timestamps, self-adaptive synchronous control under network fluctuation is achieved, and frame-level synchronization and playing continuity of a cross-device video in a variable environment are kept.

The invention discloses a cross-platform VR resource performance evaluation and compatible operation method and system, and the method comprises the steps: building a VR resource performance evaluation model based on the performance evaluation of a CPU and a GPU, and the model comprises a CPU score, a GPU score, a weight coefficient and a performance stability correction factor of a current device; the CPU score is obtained through calculation of inter-process communication and a context switching number in a current equipment system environment, the GPU score is obtained through calculation of an actual computing power value and rendering delay which are obtained through multiple iterations of a standard GLSL matrix multiplication in the current equipment system environment, and the performance stability correction factor is determined according to frame time when VR resources are operated by current equipment; when the VR resources need to be operated, the evaluation score of the VR resources operated by the current equipment is calculated through the performance evaluation model, and the corresponding configuration parameters are allocated to operate the VR resources. According to the invention, the fluency and stability of the VR picture during operation can be ensured, and the user experience is improved.

The present application relates to a smoke fire detection method based on feature matching and multi-frame time sequence fusion, which is suitable for highway fire detection scene. The method first screens out foreground change pictures based on the steady-state entropy change of video frame pixels. Then, the color and texture features of smoke fire are matched using the fast flooding algorithm to screen out candidate pictures from the foreground pictures. Next, a cache queue is established to store the candidate pictures and their timestamps, and the multi-frame time sequence image data in the queue is sent to the detection model based on the preset trigger condition. Finally, the multi-frame time sequence fusion smoke fire detection algorithm model extracts features and analyzes the time sequence fusion of the input multi-frame images, and outputs the final fire and smoke detection results. The present application realizes efficient detection through low-power cameras, reduces false alarm rate, improves complex environment monitoring distance, and has lower power cost.

The application relates to the technical fields of multimedia image processing, GPU computing power scheduling and soft power-on resource optimization, and discloses a resource occupation prediction method in a video special effect processing process, which is executed by a terminal built-in processor and comprises the following steps: collecting a video special effect frame time interval, a GPU rendering pipeline thread occupation ratio and a video memory fragment distribution ratio; performing dimensionless normalization processing on the video special effect frame time interval to obtain a special effect frame time normalization factor; counting a special effect superposition level; and calculating a special effect time coupling factor, a pipeline blocking loss factor and a total resource occupation prediction value and outputting the total resource occupation prediction value. The application solves the problem of large prediction deviation in the prior art, realizes accurate prediction through a core innovation point, guarantees reasonable allocation of video special effect rendering resources, and improves rendering stability.

The embodiment of the invention provides a live broadcast stream pushing method and device, computer equipment, a computer readable storage medium and a computer program product, and belongs to the field of network live broadcast. The live broadcast stream pushing method comprises the following steps: in response to a configuration switching instruction, determining an end timestamp of a currently pushed initial live broadcast stream; determining a start timestamp of the target live broadcast stream according to the end timestamp, the start timestamp being after the end timestamp, and a time interval between the start timestamp and the end timestamp being equal to an inter-frame interval between adjacent video frames of the initial live broadcast stream; and generating a target live broadcast stream by taking the start timestamp as a first frame timestamp, and pushing the target live broadcast stream to a streaming media server, so that an audience end pulls the target live broadcast stream from the streaming media server, finishes playing the initial live broadcast stream at an end timestamp, and plays the target live broadcast stream by taking the start timestamp as a starting point. According to the technical scheme, the watching experience of audiences and the live broadcast experience of an anchor can be improved.

The invention discloses a video facial expression recognition method based on a fuzzy time refinement network, is applied to the technical field of computer vision and artificial intelligence, and aims to solve the problems that in the prior art, the expression time sequence dynamic state is difficult to capture, the processing feature fuzziness is low, and the refinement category recognition precision is low. The method comprises the following steps: preprocessing video frames; extracting spatial features by adopting ResNet-18 (ResNet-18); modeling inter-frame time sequence dependence through LSTM; dynamically weighting key frame features by using a fuzzy attention module (FAN) in combination with Gaussian, triangle and Sigmoid membership functions; carrying out the optimization of feature clustering through the combination of cross entropy loss and TriCenter Loss; outputting an expression category; by adopting the method, the video facial expression recognition accuracy can be effectively improved.

The present invention realizes a device and a method for performing analysis by synchronizing a plurality of sensor detection data items having different frame rates and inputting the same to an analysis part. A data processing part: inputs a camera frame that is an image generated by a camera at a frame rate P (fps) and a point cloud frame that has point cloud data indicating an object position generated by a LiDAR at a frame rate Q (fps) different from the frame rate P (fps); compares a time stamp of a frame unit in the camera frame with a time stamp of a component unit in the point cloud frame; synthesizes a plurality of point cloud frames that are continuously inputted; and generates a composite point cloud frame that is synchronized with the frame rate of the camera frame.

This invention belongs to the field of computer vision and human-computer interaction technology, and relates to a gesture recognition method and system based on video stream analysis. The method includes: acquiring video capture device configuration and video stream frame images, writing frame timestamps and sampling at fixed intervals, extracting continuous keyframes and binding keyframe indices to generate a keyframe index structure; performing size normalization and contrast enhancement to obtain an effective gesture image content structure; assembling training samples and training dual models to construct a dual-model aligned result structure; performing multi-frame gesture category consistency comparison, multi-frame pose description consistency comparison, and average confidence threshold comparison to generate gesture judgment results, and constructing a loop control configuration structure. This invention effectively improves the accuracy and robustness of gesture recognition through multi-frame consistency comparison and dynamic loop control mechanisms, reduces the impact of environmental noise and instantaneous errors, and achieves efficient adaptive gesture recognition.

The invention discloses a model deformation control method, device and equipment, a medium and a product. The method comprises the steps of determining corresponding stress data in response to a stress event acting on a target model in a currently played animation; determining a global deformation modulation coefficient based on the current animation frame moment, the stress data and elastic configuration information corresponding to the target model; determining an elastic center point of the target model, and determining target offset data of at least one to-be-deformed point based on the elastic center point, the current position information, the stress data, the elastic configuration information and the global deformation modulation coefficient of the at least one to-be-deformed point in the target model; and controlling the to-be-deformed point to displace based on the target offset data so as to generate a deformation model corresponding to the current animation frame. According to the technical scheme provided by the invention, the authenticity and physical rationality of elastic deformation are ensured, the cost and complexity of art manufacturing are reduced, and the effects of improving the high efficiency, high stability and real-time performance of elastic deformation feedback are achieved.

To perform network distribution of a broadcast service in a simple mode.SOLUTION: The broadcasting company sub-system 100 has an insertion information sending part 140, and the insertion information sending part 140 supplies information for replacing commercial video / audio or video / audio conflicting with copyright with alternative contents to an insertion control part 270 of the communication network distribution sub-system 200. An insertion control section 270 replaces the commercial video / audio and the video / audio in conflict with the copyright with the alternative contents between the cut point and the restart point in the unit of frames. The cut point, the restart point, and the alternative content are supplied from the insertion information sending unit 140 of the broadcaster subsystem 100. Based on the program clock value of the timing at which the last ToT (time offset table) is received in the TS stream, the time (format of year, month, day, hour, minute, second, and millisecond, hereinafter also referred to as frame PTS time) based on the ToT of each frame is obtained, and switching is performed.SELECTED DRAWING: Figure 17

Embodiments of the present application provide a synchronization method, system and electronic device. The method comprises: a server receiving image frames sent by a plurality of IPCs and time information corresponding to the image frames, the image frames being obtained by encoding original images, the plurality of IPCs comprising a first IPC and a second IPC; when it is determined that frame times of the first IPC and the second IPC are not synchronized according to the time information corresponding to the image frames sent by the first IPC and the time information corresponding to the image frames sent by the second IPC, sending an encoding adjustment instruction to the first IPC, the encoding adjustment instruction being used to trigger the first IPC to perform encoding adjustment, so that the time at which the first IPC reads the original image of the next frame to be encoded is adjusted. In this way, the frame times of the plurality of IPCs are synchronized by the server interacting with the IPC data. Compared with the prior art, the present application does not need to rely on hardware, has good robustness, and can reduce costs and facilitate deployment of the plurality of IPCs.

The invention relates to a few-sample video abstraction method based on cross-video time sequence invariance. The method comprises the following steps that a time sequence window attention module enhances video sequence key time features; constructing a time sequence invariance enhancement module alignment support set and abstract semantic features thereof, and generating similar features of the same category; extracting multi-scale features of a query set, a support set, a support abstract and similar features of the same category through a multi-scale feature extraction module, and fusing corresponding features of each scale; through a multi-scale fusion module, performing learning attention weight adaptive fusion on the features corresponding to each scale to obtain fusion features; and fusing the features, outputting frame importance scores through a regression network, selecting representative frames, and generating a video abstract. According to the invention, by combining the time sequence invariance of the cross-video time decay, the time sequence window attention of the inter-frame time decay is enhanced, and the cross-video semantic alignment capability and the time continuity of the abstract in the video are improved.

The application discloses a QuickJS multi-interface collaborative optimization method and system of embedded equipment, relates to the technical field of JS multi-interface optimization, and comprises the following steps: S1, collecting interface task data, performing data preprocessing, identifying control difference and state change before and after the task; S2, setting a basic frame budget, constructing a task frame time consumption model, outputting a predicted frame time consumption, and comparing the current frame actual time consumption with the basic frame budget; S3, calculating the interface task priority, performing real-time sorting and scheduling, and establishing an asynchronous coroutine and task splitting and sticking mechanism; S4, outputting a structured rendering command, evaluating the redundancy of the control, and taking control adjustment measures; S5, constructing an error description vector, quantifying the deviation degree of the actual rendering result, and taking a rollback recovery measure; the problems that interface response depends on an event loop in the interaction between QuickJS and the underlying UI system, and that multiple JS interface tasks are simultaneously activated, resulting in abnormal interface rendering, are solved.

The invention discloses a QuickJS multi-interface collaborative optimization method and system of embedded equipment, and relates to the technical field of JS multi-interface optimizing.The QuickJS multi-interface collaborative optimization method comprises the steps that S1, interface task data are collected, data preprocessing is conducted, and control differences and state changes before and after a task are recognized; s2, setting a basic frame budget, constructing a task frame time consumption model, outputting predicted frame time consumption, and comparing actual time consumption of a current frame with the basic frame budget; s3, calculating interface task priorities, performing real-time sequencing and scheduling, and establishing an asynchronous coroutine and a task splitting and viscous mechanism; s4, outputting a structured rendering command, evaluating the redundancy of the control, and taking control adjustment measures; s5, constructing an error description vector, quantifying the deviation degree of an actual rendering result, and taking a rollback recovery measure; the problem that in interaction of QuickJS and an underlying UI system, interface response depends on event circulation, and interface rendering is abnormal when multiple JS interface tasks are activated at the same time is solved.

Example embodiments relate to taking images at certain predetermined angles in order to have consistent exposure throughout the images. An example embodiment includes a method. The method includes determining, using a lidar device, light intensity information of a surrounding environment of the lidar device. The light intensity information includes a plurality of angles within a threshold range of light exposure. The method also includes determining rotation times associated with each of the angles within the threshold range of light exposure. Further, the method includes based on the rotation times associated with each of the angles within the threshold range of light exposure, determining a plurality of target image times. In addition, the method includes capturing, by a camera system, a plurality of images at the plurality of target image times.

The present application relates to the technical field of intelligent control and industrial visual detection of beneficiation process, and discloses an industrial-grade ore belt boundary identification method based on multi-frame time sequence fusion. A multi-branch hollow boundary enhancement unit is constructed based on deep convolution, different convolution kernel sizes, and a Sobel operator. A feature extraction and fusion module is constructed based on unbiased convolution, time sequence cross-channel difference, the multi-branch hollow boundary enhancement unit, and multi-head attention. A feature enhancement module is constructed based on deep separable convolution and the multi-branch hollow boundary enhancement unit. A prediction module is constructed based on the multi-branch hollow boundary enhancement unit. An identification model is constructed based on the feature extraction and fusion module, the feature enhancement module, and the prediction module. Continuous multi-frame pictures in an industrial field are acquired, and after preprocessing, the multi-frame pictures are input into the identification model to obtain ore belt boundary identification results. Based on the ore belt boundary identification results, sorting is performed, and the problem that existing table sorting and identification methods cannot effectively identify ore belt boundaries is solved.

The application discloses a kind of mobile robot storage location identification method based on computer vision, it is related to machine vision technical field, including, acquisition continuous image and distortion removal, obtain luminance diagram and effective pixel mask;In frame window, the luminance sequence is counted according to line and low frequency is removed, and the frequency of LED stroboscopic frequency, modulation amplitude, intra-frame time offset and row-by-row time increment are estimated based on the joint estimation of row sequence period term and cross-frame phase difference, and the row-level modulation term is calculated;Accordingly, the luminance diagram of each frame is normalized according to line, key points and straight line segment candidates are extracted, and spatial consistency tracking is carried out through interframe homography mapping, and a stroboscopic robust key point set and a stroboscopic robust straight line set are obtained;Further estimate the main direction of the shelf and plane shaping, and the grid lines form the grid candidate area, determine the storage location number in combination with the sign analysis, and obtain the storage location pose by solving the pose from the grid corner and the physical size of the grid.

This invention discloses a rendering optimization method, program product, and storage medium based on frame interpolation and super-resolution. The method includes: acquiring an optimized super-resolution rendering frame at time t as a first rendering frame, and acquiring a low-resolution rendering frame at time t+1 in the frame display sequence as a second rendering frame; reprojecting the first rendering frame and the upsampled second rendering frame to an intermediate frame time and correcting them together to obtain corrected data for a high-resolution intermediate frame; reprojecting the features of the first and second rendering frames to the intermediate frame time and combining them with the features of the corrected data for the high-resolution intermediate frame to reconstruct a super-resolution intermediate rendering frame; reprojecting the first rendering frame to the time of the second rendering frame and correcting it together with the upsampled second rendering frame to obtain corrected data for the second rendering frame; extracting features from the corrected data for the second rendering frame; and reconstructing the super-resolution rendering frame at time t+1 based on the features of the corrected data for the second rendering frame and the features of the second rendering frame. This invention has a short running time and good rendering effect.