Multichannel video signal automatic switching method and device based on director switching station

Abstract

The invention relates to the technical field of video signal switching, and discloses a multi-channel video signal automatic switching method and device based on a director switching table. The method comprises the following steps: carrying out preprocessing and feature calculation on a plurality of video signal channels, generating a signal feature vector set and a signal quality index, and constructing a dynamic weight matrix; executing self-adaptive frame rate control and energy optimization to obtain an optimized hardware working state and a pre-heating cache state; generating scene change mapping; executing an intelligent switching decision and signal fusion to obtain a video signal and a switching log after transition processing; performing data compression and transmission optimization, and generating a compressed data stream and a transmission control parameter; according to the multi-channel video signal processing and switching method and the multi-channel video signal processing and switching device, full-process automation from video signal preprocessing to final output is realized, the multi-channel video signal processing and switching efficiency is greatly improved, and intervention of manual operation and possible errors are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of video signal switching, and in particular to a method and device for automatically switching multi-channel video signals based on a director switcher. Background Art

[0002] With the rapid development of the radio and television industry, the processing and switching of multi-channel video signals has become an indispensable part of program production. Traditional director switchers mainly rely on manual operation and face many challenges in processing complex multi-channel video signals. Manual operation is easily affected by subjective factors, and it is difficult to ensure the continuity and stability of switching, especially in a high-intensity live broadcast environment. Mistakes are more likely to occur. In addition, with the continuous improvement of video signal quality and the increasingly stringent requirements for program production, the efficiency and accuracy of manual operation can no longer meet the needs of modern radio and television production.

[0003] At the same time, the diversity and complexity of video signals are also increasing, including different resolutions, frame rates, encoding formats, etc., which brings greater difficulty to signal processing and switching. In the process of processing multi-channel video signals, how to effectively perform feature extraction, quality assessment, adaptive control and energy optimization has become a technical problem that needs to be solved urgently. Traditional switching methods are often difficult to cope with these complex scenarios, and easily lead to problems such as reduced picture quality and incoherent switching. In addition, in the signal transmission and reconstruction links, how to achieve efficient data compression and transmission optimization while ensuring video quality is also an important challenge faced by current research. Traditional methods often face bandwidth pressure and real-time problems when processing large-scale, high-quality video data, and it is difficult to meet the high requirements of modern radio and television production. Summary of the invention

[0004] The present application provides a method and device for automatic switching of multi-channel video signals based on a director switcher, which is used to realize full-process automation from video signal preprocessing to final output, improve the efficiency of multi-channel video signal processing and switching, and reduce human intervention and possible errors.

[0005] In a first aspect, the present application provides a method for automatically switching a multi-channel video signal based on a director switcher, the method comprising:

[0006] Preprocess and calculate features of multiple video signal channels, generate signal feature vector sets and signal quality indicators, and construct corresponding dynamic weight matrices;

[0007] Based on the signal feature vector set and the signal quality indicator, performing adaptive frame rate control and energy optimization to obtain an optimized hardware working state and a preheating cache state;

[0008] According to the optimized hardware working state and the preheated cache state, multi-scale feature extraction and matching are performed on the video frame to generate a scene change map;

[0009] Based on the scene change mapping and the dynamic weight matrix, intelligent switching decision and signal fusion are performed to obtain a transition-processed video signal and a switching log;

[0010] Performing data compression and transmission optimization on the video signal after the transition processing, and adjusting the compression strategy according to the switching log to generate a compressed data stream and transmission control parameters;

[0011] Signal demultiplexing and reconstruction are performed based on the compressed data stream and the transmission control parameters, and a target video signal is output.

[0012] In a second aspect, the present application provides a multi-channel video signal automatic switching device based on a director switcher, the multi-channel video signal automatic switching device based on a director switcher comprising:

[0013] A preprocessing module is used to preprocess and calculate features of multiple video signal channels, generate signal feature vector sets and signal quality indicators, and construct corresponding dynamic weight matrices;

[0014] An optimization module, configured to perform adaptive frame rate control and energy optimization based on the signal feature vector set and the signal quality index, to obtain an optimized hardware working state and a preheating cache state;

[0015] A matching module, configured to extract and match multi-scale features of video frames according to the optimized hardware working state and the preheated cache state, and generate a scene change map;

[0016] A fusion module, used to perform intelligent switching decision and signal fusion based on the scene change mapping and the dynamic weight matrix to obtain a transition-processed video signal and a switching log;

[0017] A generating module, used for performing data compression and transmission optimization on the video signal after the transition processing, adjusting the compression strategy according to the switching log, and generating a compressed data stream and transmission control parameters;

[0018] The output module is used to perform signal demultiplexing and reconstruction based on the compressed data stream and the transmission control parameters, and output a target video signal.

[0019] A third aspect of the present application provides a multi-channel video signal automatic switching device based on a director switchboard, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory to enable the multi-channel video signal automatic switching device based on the director switchboard to execute the above-mentioned multi-channel video signal automatic switching method based on the director switchboard.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned method for automatic switching of multi-channel video signals based on a director switch.

[0021] In the technical solution provided by the present application, by preprocessing and feature calculation of multiple video signal channels, generating signal feature vector sets and signal quality indicators, and performing adaptive frame rate control and energy optimization based on signal features and quality indicators, the operating efficiency can be effectively improved, energy consumption can be reduced, and the quality of video processing can be guaranteed. By extracting and matching multi-scale features of video frames and generating scene change mapping, the changes in video content can be captured more accurately. Based on scene change mapping and dynamic weight matrix, intelligent switching decisions and signal fusion can be performed to achieve smoother and more natural picture transitions and enhance the viewing experience. Data compression and transmission optimization of the video signal after transition processing, and adjusting the compression strategy according to the switching log, can improve transmission efficiency while ensuring video quality and reduce bandwidth pressure. Through the optimized signal demultiplexing and reconstruction process, the original video signal can be quickly and accurately restored to ensure the quality of the final output video. The present application realizes the automation of the entire process from video signal preprocessing to final output, greatly improving the efficiency of multi-channel video signal processing and switching, and reducing human intervention and possible errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 A schematic diagram of an embodiment of a method for automatically switching multi-channel video signals based on a director switcher in an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of an embodiment of a multi-channel video signal automatic switching device based on a director switch in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiment of the present application provides a method and device for automatically switching multi-channel video signals based on a director switcher. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the method for automatically switching a multi-channel video signal based on a director switcher includes:

[0027] Step S101, preprocessing and feature calculation are performed on multiple video signal channels to generate a signal feature vector set and a signal quality index, and construct a corresponding dynamic weight matrix;

[0028] It is understandable that the execution subject of the present application can be a multi-channel video signal automatic switching device based on a director switch, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0029] Specifically, the original video signal is subjected to noise reduction processing by spatial domain filtering technology to reduce the noise component in the video signal and obtain the video signal after noise reduction. The video signal after noise reduction is subjected to color correction to correct the color distortion in the video signal and ensure the accuracy of color restoration to obtain the video signal after color correction. The video signal after color correction is subjected to resolution standardization processing to eliminate the resolution difference between different video signal channels by unifying the resolution of the video signal to obtain a standardized video signal. Texture feature extraction is performed on the standardized video signal, and a texture feature map is generated by analyzing the texture information of the video signal. The motion vector field is calculated based on the texture feature map and the standardized video signal, and the motion intensity feature is obtained by analyzing the motion vector between the video frames. At the same time, the standardized video signal is subjected to color histogram analysis and clustering, and the color distribution feature is obtained by statistically analyzing the distribution of different colors in the video image. These features are helpful in describing the color information in the video signal. The scene change frequency is calculated for the motion intensity feature and the color distribution feature, and the scene change feature is obtained by analyzing the frequency of scene changes in the video signal. At the same time, the standardized video signal is discrete cosine transformed and quantized, and the signal-to-noise ratio and structural similarity index are calculated to evaluate the quality of the video signal and obtain the signal quality index. The texture feature map, motion intensity feature, color distribution feature and scene change feature are multi-dimensional feature vectors constructed, and the signal feature vector set is obtained by integrating these features into a multi-dimensional vector. The signal feature vector set and signal quality index are subjected to principal component analysis, and the main feature components are extracted through dimensionality reduction and feature extraction to simplify the complexity of the feature vector. At the same time, the fuzzy comprehensive evaluation method is used to comprehensively evaluate the feature vector to obtain the dynamic weight matrix. The dynamic weight matrix generates a matrix that reflects the importance of the video signal features by comprehensively considering the weights of each feature.

[0030] Step S102: Based on the signal feature vector set and the signal quality index, adaptive frame rate control and energy optimization are performed to obtain an optimized hardware working state and a preheating cache state;

[0031] Specifically, the signal feature vector set is compressed to obtain a compressed feature vector. By reducing the dimension of the feature vector, the computational complexity is reduced. According to the compressed feature vector and the signal quality index, a frame rate prediction model is constructed, and the frame rate prediction value of each video signal channel is calculated. The frame rate prediction value is smoothed to obtain a frame rate control parameter. These parameters are used to calculate the optimal operating frequency of the processor core based on the current hardware load information, and then generate a frequency adjustment instruction. This process optimizes the use of energy by adjusting the operating frequency of the processor. The use information of hardware resources is analyzed, and the future resource demand is predicted to obtain a resource demand prediction value. The resource demand prediction value is combined with the frequency adjustment instruction to optimize the switching strategy of the functional unit and form a functional unit configuration scheme. By intelligently managing the use of hardware resources, energy efficiency and system performance are improved. At the same time, by analyzing the historical scene change data and identifying the typical scene pattern, the category label of each scene is obtained. According to the scene category label and the characteristics of the current scene, a preheating data list is generated. The elements in these data lists are sorted according to priority, and the optimal loading order is calculated to form a cache preheating sequence. According to the cache preheating sequence and functional unit configuration plan, hardware resources are reasonably allocated to ensure that each functional unit can operate in the most suitable state, thereby obtaining an optimized hardware working state and preheating cache state.

[0032] Step S103: extract and match multi-scale features of the video frames according to the optimized hardware working state and preheating cache state to generate a scene change map;

[0033] Specifically, according to the optimized hardware working state, the video frame is multi-scale decomposed to obtain multiple image sequences of different scales, and each scale image sequence contains multiple levels from the original image to the low-resolution image. Feature extraction is performed on each scale image in the multi-scale image sequence to obtain a target point set and a descriptor set. The feature points and descriptors describe the local features of the image at different scales. While extracting features, predefined features are loaded from the cache according to the preheating cache state. The previously extracted target point set is filtered using the predefined features to obtain a filtered target point set. The filtered target point set is distributed and optimized to ensure that the feature points are more evenly distributed in the image, to obtain an evenly distributed feature point set, and to reduce the situation where the feature points are too dense in some areas and sparse in other areas. Based on the evenly distributed feature point set and the corresponding descriptor set, a feature database is constructed and a feature index structure is generated. The feature database stores all feature points and their descriptors, and the feature index structure is used to quickly find and match feature points. Based on the feature database and the feature index structure, the feature points between consecutive frames are partitioned and matched to obtain initial matching results, and the corresponding relationship between the feature points between consecutive frames is preliminarily determined. Based on the initial matching results, a geometric consistency matching set is generated. Through geometric consistency verification, the spatial consistency and rationality of the matched feature points are ensured, and false matches are eliminated. The motion information of the geometric consistency matching set is calculated to obtain a motion vector map. The motion vector map describes the movement of feature points between consecutive frames. Based on the motion vector map, the image is segmented and analyzed to determine the scene changes in different areas of the image and obtain the scene change area. The scene change area is encoded and feature fused. The encoding process converts the spatial information and motion information of the scene change area into a digital representation, while the feature fusion combines multiple features to form a unified scene change description, and finally obtains the scene change mapping.

[0034] Step S104: Based on the scene change mapping and the dynamic weight matrix, intelligent switching decision and signal fusion are performed to obtain a video signal and a switching log after transition processing;

[0035] Specifically, the scene change mapping is analyzed for regional importance, and the scene importance score is obtained to evaluate the degree of attraction of each region to the audience's attention. Based on the scene importance score and the dynamic weight matrix, the comprehensive score of each video signal channel is calculated, and a channel priority list is generated. The channel priority list reflects the importance ranking of each video signal channel and determines the priority of signal switching. The channel priority list is smoothed in time to avoid the instability and incoherence caused by frequent switching. Through smoothing, a more stable and reasonable switching candidate sequence is obtained. According to the switching candidate sequence and the preset program structure rules, the initial switching plan is generated. These program structure rules include specific requirements for switching frequency, duration and scene type to ensure that the switching plan meets the specifications and style of program production. After the initial switching plan is generated, it is conflict detected and adjusted to ensure the feasibility and rationality of the plan. The purpose of conflict detection is to find possible resource conflicts or logical errors, and the adjustment process is to resolve these conflicts and obtain an optimized switching plan. According to the optimized switching plan, the corresponding video signal channel is selected and the source signal to be fused is generated. In the signal fusion stage, the source signals to be fused are aligned in time and space to ensure the consistency of different video signals in time and space, and avoid visual discomfort caused by asynchrony or inconsistent positions. According to the aligned signals, the optimal transition effect parameters are calculated. These parameters include the type of transition, duration, and effect intensity, etc. Through these parameters, specific transition effect instructions are obtained. Transition effect instructions are an important basis for the actual execution of transition processing, guiding the system on how to achieve smooth switching between different video signals. The transition effect instructions are executed through the aligned signals to obtain the video signal after transition processing. At the same time, the optimized switching scheme and the execution process of the transition effect instructions are recorded to generate a switching log. The switching log records the time point, signal source, transition effect and other information of each signal switching.

[0036] Step S105: compressing and optimizing the transmission of the video signal after the transition processing, adjusting the compression strategy according to the switching log, and generating a compressed data stream and transmission control parameters;

[0037] Specifically, a temporal and spatial analysis is performed on the video signal after the transition processing to obtain a signal complexity map, which reflects the complexity of the video signal at different times and spaces. According to the signal complexity map and the switching log, the video frame is divided into different compression priority areas to generate a regional compression strategy. The regional compression strategy allocates different compression resources according to the importance and complexity of the video content to achieve an optimized compression effect. The video signal is subjected to a dynamic quantization parameter matrix analysis to obtain an initial quantization result. The dynamic quantization parameter matrix analysis balances the compression rate and image quality by adjusting the quantization parameters. According to the regional compression strategy, the initial quantization result is refined and adjusted to ensure that the important area is retained with higher quality, while the secondary area can be compressed more highly to obtain an optimized quantization result. The optimized quantization result is entropy encoded to obtain the original data stream. Entropy encoding is a key step in data compression, which reduces the amount of data through efficient encoding. According to the scene switching information in the switching log, the original data stream is segmented to obtain a segmented data stream. The segmentation process divides the data stream so that the transmission strategy can be adjusted more flexibly when a scene switch occurs. The segmented data stream is error-resiliently encoded to obtain a compressed data stream. Error recovery coding improves the reliability of data transmission. By adding redundant information, it enables effective recovery when errors occur during transmission. According to the network status and switching log, the transmission priority of the data packet is calculated to obtain the transmission scheduling strategy. The transmission scheduling strategy ensures that important data can be transmitted first through priority control, improving the overall transmission efficiency and stability. The compressed data stream is packetized and encrypted to obtain the data packet to be transmitted. The packetization process divides the large data stream into small data packets for easy transmission and management. The encryption process ensures that the data is not stolen or tampered with during transmission, protecting the security of the data. According to the transmission scheduling strategy, the transmission control parameters including the transmission timing and retransmission mechanism are generated. The transmission timing controls the order and time of sending the data packet, and the retransmission mechanism retransmits the data packet when it is lost to ensure data integrity and transmission reliability.

[0038] Step S106: perform signal demultiplexing and reconstruction based on the compressed data stream and the transmission control parameters, and output the target video signal.

[0039] Specifically, the data packets to be transmitted are sorted and integrity checked according to the transmission control parameters to ensure that the data packets are arranged in the correct order and without loss, so as to obtain an ordered sequence of data packets. The ordered sequence of data packets is decrypted and unpacked to extract the compressed data stream. According to the error recovery information in the transmission control parameters, error detection and correction are performed on the compressed data stream to obtain a corrected data stream. The errors that may occur in the transmission are detected and repaired by using redundant information to ensure the integrity and accuracy of the data. The corrected data stream is entropy decoded to restore the original quantized data. Entropy decoding is a key step in data decompression and can convert the compressed bit stream back to quantized data. According to the quantized data and the preset inverse quantization matrix, an inverse quantization operation is performed to obtain frequency domain coefficients. The frequency domain coefficients are the representation of image data in the frequency domain. Through the inverse quantization operation, their original values ​​can be restored. The frequency domain coefficients are inversely transformed to obtain an initially reconstructed video frame. Inverse transformation is the process of converting frequency domain data back to spatial domain data, thereby restoring the original image structure of the video frame. According to the scene switching information in the transmission control parameters, the initially reconstructed video frame is segmented to obtain a segmented video frame. Deblocking and de-ringing are performed on the segmented video frames to obtain optimized video frames. Deblocking can eliminate block artifacts that occur during compression, while de-ringing can reduce the ringing effect at the edge of the image and improve the visual quality of the image. Motion compensation and interpolation are performed based on the optimized video frames and adjacent frame information to obtain a smooth transition video sequence. Motion compensation and interpolation technology can generate transition frames based on the motion information of adjacent frames, making the video sequence smoother and more natural during scene switching and motion. The smooth transition video sequence is converted and corrected to output the target video signal. The conversion and correction process includes color space conversion, brightness and contrast adjustment, etc., to ensure that the final output video signal meets the requirements of the display device and the visual habits of the audience.

[0040] In the embodiment of the present application, by preprocessing and feature calculation of multiple video signal channels, generating signal feature vector sets and signal quality indicators, and performing adaptive frame rate control and energy optimization based on signal features and quality indicators, the operating efficiency can be effectively improved, energy consumption can be reduced, and the quality of video processing can be guaranteed. By extracting and matching multi-scale features of video frames and generating scene change mapping, the changes in video content can be captured more accurately. Based on scene change mapping and dynamic weight matrix, intelligent switching decisions and signal fusion can be performed to achieve smoother and more natural picture transitions and enhance the viewing experience. Data compression and transmission optimization are performed on the video signal after transition processing, and the compression strategy is adjusted according to the switching log, which can improve transmission efficiency and reduce bandwidth pressure while ensuring video quality. Through the optimized signal demultiplexing and reconstruction process, the original video signal can be quickly and accurately restored to ensure the quality of the final output video. The present application realizes the automation of the entire process from video signal preprocessing to final output, greatly improving the efficiency of multi-channel video signal processing and switching, and reducing the intervention and possible errors of human operation.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) performing spatial domain filtering processing on each video signal channel to obtain a denoised video signal, and performing color correction on the denoised video signal to obtain a color-corrected video signal;

[0043] (2) performing resolution standardization processing on the color-corrected video signal to obtain a standardized video signal, and performing texture feature extraction on the standardized video signal to obtain a texture feature map;

[0044] (3) Calculating the motion vector field based on the texture feature map and the standardized video signal to obtain the motion intensity feature, and performing color histogram analysis and clustering on the standardized video signal to obtain the color distribution feature;

[0045] (4) Calculating the scene change frequency based on the motion intensity features and the color distribution features to obtain the scene change features;

[0046] (5) performing discrete cosine transform and quantization on the standardized video signal and calculating the signal-to-noise ratio and structural similarity index to obtain a signal quality index;

[0047] (6) constructing a multi-dimensional feature vector of the texture feature map, motion intensity feature, color distribution feature, and scene change feature to obtain a signal feature vector set;

[0048] (7) Perform principal component analysis and fuzzy comprehensive evaluation on the signal feature vector set and signal quality index to obtain a dynamic weight matrix.

[0049] Specifically, spatial domain filtering technology is applied to each video signal channel. Spatial domain filtering is a method of removing noise by operating the pixel values ​​of an image. Common filtering methods include mean filtering, median filtering, and Gaussian filtering. Taking Gaussian filtering as an example, its principle is to perform a convolution operation on the image through a Gaussian kernel so that the noise in the image is smoothed. The formula for Gaussian filtering is as follows:

[0050]

[0051] Among them, G(x,y) is the Gaussian kernel function, σ is the standard deviation, and x and y are the offsets relative to the center point. By performing Gaussian filtering on the video signal, the random noise in the image is effectively reduced to obtain a denoised video signal. The denoised video signal is color corrected to adjust the color balance of the image so that the color of the image is more realistic and natural. Common color correction methods include white balance correction and gamma correction. White balance correction adjusts the RGB channels of the image so that white objects in the image appear white. Gamma correction adjusts the brightness distribution of the image to make the contrast of the image more appropriate. The formula is as follows:

[0052]

[0053] Among them, I out is the corrected image pixel value, I i n is the original image pixel value, and γ is the gamma value. Through gamma correction, the brightness and contrast of the image can be improved to obtain a color-corrected video signal. The color-corrected video signal is subjected to resolution standardization processing to unify video signals of different resolutions to the same resolution for subsequent processing. Resolution standardization can be achieved through image interpolation methods. Common interpolation methods include bilinear interpolation and bicubic interpolation. Through resolution standardization processing, a standardized video signal is obtained. Texture feature extraction is performed on the standardized video signal. Texture feature is an important indicator to describe the texture information in the image. Common texture feature extraction methods include grayscale co-occurrence matrix, local binary pattern (LBP), etc. Taking the grayscale co-occurrence matrix as an example, it extracts the texture features of the image by calculating the co-occurrence relationship of the grayscale values ​​in the image. The obtained texture feature map can be used for subsequent feature analysis. The motion vector field is calculated based on the texture feature map and the standardized video signal to obtain the motion intensity feature. The motion vector field describes the motion information between video frames. Common methods include optical flow method and block matching method. The optical flow method obtains the motion intensity feature by calculating the motion vector of the image pixel. The formula is as follows:

[0054]

[0055] Among them, v is the motion vector, ΔI is the grayscale change of the image, and Δt is the time interval. The motion information of each pixel is obtained by calculating the motion vector field, and the motion intensity feature is obtained. At the same time, the color histogram analysis and clustering are performed on the standardized video signal to obtain the color distribution feature. The color histogram is a statistical distribution of different colors in the image. The color characteristics of the image are obtained by color histogram analysis. Clustering algorithms, such as K-means clustering, perform clustering analysis on the color histogram to obtain the main color distribution characteristics of the image. The scene change frequency is calculated for the motion intensity feature and the color distribution feature to obtain the scene change feature. The scene change frequency reflects the frequency of scene switching in the video signal, which can be calculated by analyzing the changes in the motion intensity feature and the color distribution feature. The scene change feature is obtained by calculating the scene change frequency. The standardized video signal is discrete cosine transformed and quantized, and the signal-to-noise ratio and structural similarity index are calculated to obtain the signal quality index. The discrete cosine transform converts the image from the spatial domain to the frequency domain for compression and feature extraction. The formula is as follows:

[0056]

[0057] Among them, F(u,v) is the frequency domain coefficient, I(x,y) is the spatial domain pixel value, and N is the image size. The frequency domain coefficient of the image is obtained by discrete cosine transform and quantization. Signal-to-noise ratio and structural similarity index are important indicators for evaluating image quality. The signal-to-noise ratio measures the ratio of the signal strength to the noise strength of the image, and the structural similarity index measures the structural similarity of the image. Multi-dimensional feature vectors are constructed for texture feature maps, motion intensity features, color distribution features, and scene change features to obtain a signal feature vector set. Principal component analysis and fuzzy comprehensive evaluation are performed on the signal feature vector set and signal quality indicators to obtain a dynamic weight matrix. Principal component analysis simplifies the complexity of feature vectors through dimensionality reduction and feature extraction, and fuzzy comprehensive evaluation integrates various features to generate a dynamic weight matrix for intelligent switching decisions and signal fusion.

[0058] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0059] (1) compressing the signal feature vector set to obtain a compressed feature vector, and constructing a frame rate prediction model based on the compressed feature vector and the signal quality index and calculating the frame rate prediction value of each video signal channel;

[0060] (2) Smoothing the frame rate prediction value to obtain a frame rate control parameter, and calculating the optimal operating frequency of the processor core according to the frame rate control parameter and the current hardware load to obtain a frequency adjustment instruction;

[0061] (3) Analyze the hardware resource usage information and predict future resource demand to obtain a resource demand prediction value, and optimize the switching strategy of the functional unit based on the resource demand prediction value and the frequency adjustment instruction to obtain a functional unit configuration plan;

[0062] (4) Analyze historical scene change data and identify typical scene patterns to obtain scene category labels, and generate a preheating data list based on the scene category labels and current scene features;

[0063] (5) Prioritize and calculate the optimal loading order of the elements in the preheating data list to obtain a cache preheating sequence;

[0064] (6) Allocate hardware resources according to the cache preheating sequence and the functional unit configuration scheme to obtain an optimized hardware working state and preheating cache state.

[0065] Specifically, the signal feature vector set is compressed to obtain a compressed feature vector. The signal feature vector set is a multi-dimensional description of the video signal. Compression can reduce its dimension and reduce computational complexity. A frame rate prediction model is constructed based on the compressed feature vector and signal quality indicators. The frame rate prediction model predicts the frame rate of each video signal channel by analyzing historical data and current features. Commonly used methods include linear regression and time series analysis. Assuming a linear regression model is used, its formula is as follows:

[0066]

[0067] Among them, Y is the compressed feature vector, is the predicted frame rate, a is the regression coefficient, and b is the bias term. Through regression analysis, the frame rate prediction value of each video signal channel is obtained. The frame rate prediction value is smoothed to reduce the prediction error and fluctuation. The commonly used smoothing method is the exponentially weighted moving average, and its formula is as follows:

[0068]

[0069] Among them, S t is the current frame rate control parameter, α is the smoothing coefficient, is the current predicted frame rate, S t-1 is the smoothed value of the previous moment. Through smoothing, a more stable frame rate control parameter is obtained. According to the frame rate control parameter and the current hardware load, the optimal operating frequency of the processor core is calculated. Assuming that the energy consumption of the processor is quadratically related to the operating frequency, its power consumption model can be expressed as:

[0070] P = kf 2 ;

[0071] Where P is the power consumption, k is a constant related to the processor characteristics, and f is the operating frequency. According to the frame rate control parameters and the hardware load, the frequency of the processor is optimized to obtain the frequency adjustment instruction. The hardware resource usage information is analyzed and future resource requirements are predicted. Resource demand prediction can be achieved through time series analysis methods, such as the autoregressive integral moving average model. After the resource demand value is predicted, the switching strategy of the functional unit is optimized in combination with the frequency adjustment instruction to ensure that the resource utilization efficiency is maximized and the functional unit configuration scheme is obtained. The historical scene change data is analyzed and typical scene patterns are identified. Through clustering algorithms, such as K-means, the scene change data can be divided into different categories to obtain scene category labels. According to the current scene characteristics and the scene category labels, a preheating data list is generated. The preheating data list includes data that needs to be loaded first in different scenarios. The elements in the preheating data list are prioritized and the optimal loading order is calculated. The preheating data is sorted using a priority queue algorithm. According to the sorting results, a cache preheating sequence is formed. According to the cache preheating sequence and the functional unit configuration scheme, hardware resources are allocated. When allocating resources, you need to consider resource availability and priority to ensure that critical tasks are executed first and that the hardware is optimized and the cache is preheated.

[0072] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0073] (1) According to the optimized hardware working state, the video frame is decomposed into multiple scales to obtain a multi-scale image sequence, and features are extracted for each scale image in the multi-scale image sequence to obtain a target point set and a descriptor set;

[0074] (2) According to the preheating cache state, predefined features are loaded from the cache, and the target point set is screened to obtain a screened target point set;

[0075] (3) Optimizing the distribution of the screened target point set to obtain a uniformly distributed feature point set, and constructing a feature database and generating a feature index structure based on the uniformly distributed feature point set and descriptor set;

[0076] (4) performing partition matching on feature points between consecutive frames according to the feature database and feature index structure to obtain initial matching results, and generating a geometric consistency matching set according to the initial matching results;

[0077] (5) Calculate motion information of the geometric consistency matching set to obtain a motion vector map, and perform region segmentation and analysis based on the motion vector map to obtain the scene change region;

[0078] (6) Encode and fuse features of the scene change area to obtain a scene change map.

[0079] Specifically, the video frame is decomposed at multiple scales, and a series of images with decreasing scales are generated by filtering and downsampling the image at different scales. These images can represent different levels of detail of the original image. Commonly used methods include Gaussian pyramid and Laplacian pyramid. Taking Gaussian pyramid as an example, its formula is as follows:

[0080]

[0081] Among them, I8(x,y) is the image of the lth layer, w(i,j) is the Gaussian kernel function, k is the kernel size, I 8-1(x, y) is the image of the l-1th layer. Through the Gaussian pyramid, a multi-scale image sequence is obtained, and each scale image contains different details of the original image. Feature extraction is performed on each scale image in the multi-scale image sequence to find the key points and their descriptors in the image. These features are used for matching and tracking. Common feature extraction methods include SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features). Taking SIFT as an example, its feature extraction process includes detecting extreme points in scale space, accurately locating the key point allocation direction, and generating descriptors to obtain target point sets and descriptor sets. These feature points and descriptors describe the local features of the image at different scales. According to the preheating cache state, predefined features are loaded from the cache, and the target point set is filtered to obtain a filtered target point set. Predefined features are features pre-stored in the cache based on previous experience and knowledge, which are used to accelerate the feature matching process. The purpose of filtering is to remove redundant or unimportant feature points, retain only those feature points that match the predefined features, and improve the accuracy and efficiency of feature matching. The filtered target point set is distributed optimized to obtain a uniformly distributed feature point set. The purpose of distribution optimization is to ensure that the distribution of feature points in the image is more uniform, and to avoid the situation where feature points are too dense in some areas and sparse in other areas. A grid-based distribution optimization method can be used to divide the image into several grids, and a certain number of feature points are retained in each grid to achieve uniform distribution. Through optimization, a uniformly distributed feature point set is obtained. According to the uniformly distributed feature point set and descriptor set, a feature database is constructed and a feature index structure is generated. The feature database is used to store all feature points and their descriptors, while the feature index structure is used to quickly find and match feature points. Commonly used index structures include kd trees and FLANN (fast nearest neighbor search library), which can efficiently perform feature matching and search. Based on the feature database and feature index structure, partition matching is performed on feature points between consecutive frames to obtain initial matching results. The purpose of partition matching is to improve the efficiency and accuracy of matching by dividing the image into several regions and performing feature matching in each region. Through the feature index structure, the nearest neighbor feature point of each feature point is quickly found to obtain the initial matching result. Based on the initial matching result, a geometric consistency matching set is generated. Geometric consistency matching verifies the geometric relationship between feature points, eliminates incorrectly matched feature points, and improves matching accuracy. Common methods include RANSAC (Random Sampling Consistency), which finds the largest consistency set through random sampling and iterative optimization to obtain a geometric consistency matching set. The motion information of the geometric consistency matching set is calculated to obtain a motion vector map. The motion vector map describes the motion information between video frames and can be obtained by calculating the displacement between matching feature points. The formula is as follows:

[0082]

[0083] Among them, v i is the motion vector of the i-th feature point, and is the coordinate of the feature point at time t, and is the coordinate of the feature point at time t+1. The motion vector of each feature point is obtained by calculating the motion information. The scene change region is obtained by performing region segmentation and analysis based on the motion vector map. The purpose of region segmentation is to divide the image into several regions, each of which contains similar motion information. Commonly used methods include graph-based segmentation and clustering-based segmentation, through which scene change regions are obtained. The scene change region describes the part of the image that has changed significantly and is the key to subsequent processing. The scene change region is encoded and feature fused to obtain a scene change map. The purpose of encoding is to convert the information of the scene change region into a digital representation, while feature fusion combines multiple features to form a unified scene change description, and finally obtains a scene change map.

[0084] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0085] (1) Performing regional importance analysis on scene change mapping to obtain scene importance scores, and calculating the comprehensive scores of each video signal channel and generating a channel priority list based on the scene importance scores and dynamic weight matrix;

[0086] (2) performing time-series smoothing on the channel priority list to obtain a switching candidate sequence, and generating an initial switching plan based on the switching candidate sequence and a preset program structure rule;

[0087] (3) Conflict detection and adjustment are performed on the initial switching plan to obtain an optimized switching plan, and according to the optimized switching plan, a corresponding video signal channel is selected and a source signal to be fused is generated;

[0088] (4) performing spatiotemporal alignment processing on the source signal to be fused to obtain an aligned signal, and calculating the optimal transition effect parameter based on the aligned signal to obtain a transition effect instruction;

[0089] (5) Execute the transition effect instruction through the aligned signal to obtain the video signal after transition processing, and record the execution process of the optimized switching scheme and the transition effect instruction to generate a switching log.

[0090] Specifically, the importance of each region in the video frame is determined by performing a regional importance analysis on the scene change map, for example, using a method based on a visual attention model and a method based on semantic information. The method based on the visual attention model determines which regions are more likely to attract the audience's attention by calculating the saliency map of the image. The formula is as follows:

[0091]

[0092] Among them, S(x,y) is the significance value, w i is the feature weight, f i (x, y) is the value of the ith feature at position (x, y), and N is the number of features. The importance score of each region is obtained by saliency analysis of the scene change mapping. Based on the scene importance score and the dynamic weight matrix, the comprehensive score of each video signal channel is calculated. The dynamic weight matrix reflects the importance of different features and can be obtained through principal component analysis or fuzzy comprehensive evaluation method. The calculation formula of the comprehensive score is as follows:

[0093]

[0094] Among them, C ； is the comprehensive score of the jth video signal channel, W ；: is the weight value in the dynamic weight matrix, S ；: is the scene importance score of the kth feature of the jth channel, and M is the number of features. By calculating the comprehensive score, the importance of each video signal channel is obtained. A channel priority list is generated based on the comprehensive score. The channel priority list is sorted from high to low according to the comprehensive score, reflecting the priority of each video signal channel. The channel priority list is time-smoothed to reduce the fluctuation of priority changes. For example, the exponentially weighted moving average method is used, and its formula is as follows:

[0095] P t =αC t +21-α5P t-1 ;

[0096] Among them, P t is the smoothing priority at the current moment, α is the smoothing coefficient, C t is the comprehensive score at the current moment, P t-1is the smoothing priority of the previous moment. Through time sequence smoothing, a more stable switching candidate sequence is obtained. According to the switching candidate sequence and the preset program structure rules, an initial switching plan is generated. The program structure rules include specific requirements for switching frequency, duration and scene type to ensure that the switching plan meets the specifications and style of program production. The initial switching plan needs to meet these rules and try to select video signal channels with high priority. Conflict detection and adjustment are performed on the initial switching plan to ensure the feasibility and rationality of the plan. The purpose of conflict detection is to find possible resource conflicts or logical errors, such as two channels occupying the same resource at the same time. The adjustment process is to resolve these conflicts and obtain an optimized switching plan by reallocating resources or adjusting the switching order. According to the optimized switching plan, the corresponding video signal channel is selected and the source signal to be fused is generated. The source signal to be fused is a plurality of video signals selected according to the switching plan. These signals need to be aligned in time and space to ensure the synchronization and consistency between the signals. The time and space alignment processing includes time alignment and space alignment. Time alignment is achieved by adjusting the frame rate and timestamp, and space alignment is achieved by adjusting the image position and size. According to the aligned signal, the optimal transition effect parameters are calculated. The transition effect parameters include the type, duration and effect strength of the transition, etc. Through these parameters, specific transition effect instructions are obtained. The calculation of the transition effect parameters can be achieved by minimizing the transition error, and the formula is as follows:

[0097]

[0098] Where E is the transition error, and are the pixel values ​​of the two frames before and after the transition, and T is the number of transition frames. The optimal transition effect parameters are obtained by minimizing the transition error. The transition effect instructions are executed through the aligned signal to obtain the video signal after transition processing. The video signal after transition processing should be smooth, natural and high-quality, and can meet the visual needs of the audience and the standards of program production. At the same time, the execution process of the optimized switching scheme and the transition effect instructions is recorded to generate a switching log. The switching log records the time point, signal source, transition effect and other information of each signal switching.

[0099] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0100] (1) Performing spatiotemporal analysis on the video signal after transition processing to obtain a signal complexity map, and dividing the compression priority areas of the video frame according to the signal complexity map and the switching log to obtain a regional compression strategy;

[0101] (2) Performing dynamic quantization parameter matrix analysis on the video signal to obtain an initial quantization result, and refining and adjusting the initial quantization result according to the regional compression strategy to obtain an optimized quantization result;

[0102] (3) performing entropy coding on the optimized quantization result to obtain an original data stream, and performing segmentation processing on the original data stream according to the scene switching information in the switching log to obtain a segmented data stream;

[0103] (4) Error recovery coding is performed on the segmented data stream to obtain a compressed data stream, and the transmission priority of the data packet is calculated according to the network status and switching log to obtain a transmission scheduling strategy;

[0104] (5) The compressed data stream is packetized and encrypted to obtain data packets to be transmitted, and transmission control parameters including transmission timing and retransmission mechanism are generated according to the transmission scheduling strategy.

[0105] Specifically, the video signal after transition processing is subjected to spatiotemporal analysis. Spatiotemporal analysis determines the complexity of the signal by evaluating the changes of the video signal in time and space. The complexity map is an image used to represent the complexity of the video signal at different positions and time points. The complexity map can be obtained by calculating the change and difference of each pixel. The formula is as follows:

[0106] C(x,y,t)=|I(x,y,t)-I(x,y,t-1)|;

[0107] Among them, C(x,y,t) is the complexity value at position (x,y) and time t, and I(x,y,t) is the pixel value at position (x,y) and time t. Through the complexity graph, you can intuitively see the areas with large changes in the video signal. According to the signal complexity graph and the switching log, the compression priority areas of the video frame are divided. The switching log records the detailed information of the video signal switching, including the switching time and switching area. This information can help determine which areas require higher compression priority. Based on the complexity graph and the switching log, the video frame is divided into different compression priority areas. High complexity areas and frequently switched areas are given higher compression priorities, while low complexity areas and less switched areas are given lower compression priorities. The obtained regional compression strategy can effectively allocate compression resources and improve the compression efficiency of the video signal. The video signal is subjected to dynamic quantization parameter matrix analysis to obtain the initial quantization result. Dynamic quantization parameter matrix analysis balances compression rate and image quality by adjusting the quantization parameter. The selection of quantization parameters can significantly affect the compression results. The formula is as follows:

[0108]

[0109] Among them, Q(i,j) is the quantization parameter at position (i,j), QSTUV (i,j) is the basic quantization parameter, Q UWT8V (i,j) is the quantization scale factor. The initial quantization result is obtained by analyzing the dynamic quantization parameter matrix. The initial quantization result is refined and adjusted according to the regional compression strategy. The quantization parameter is optimized according to the compression priority of different regions. A smaller quantization parameter is used in a high compression priority region to retain more details; a larger quantization parameter is used in a low compression priority region to improve the compression rate. After refinement and adjustment, the optimized quantization result is obtained. The optimized quantization result is entropy encoded to obtain the original data stream. Entropy coding is a key step in data compression. It reduces the amount of data through efficient coding. Common entropy coding methods include Huffman coding and arithmetic coding. Through entropy coding, the quantized data is converted into a compact bit stream to obtain the original data stream. According to the signal complexity graph and the scene switching information in the switching log, the original data stream is segmented. Segmentation is to divide the original data stream into several small segments, each corresponding to a video frame or scene switching point. This helps to flexibly adjust the data stream during transmission and improve transmission efficiency. Error recovery coding is performed on the segmented data stream to obtain a compressed data stream. Error recovery coding adds redundant information to enable effective recovery when errors occur during transmission. Common methods include forward error correction (FEC) and cyclic redundancy check (CRC). Error recovery coding improves the reliability of data transmission. According to the network status and switching log, the transmission priority of the data packet is calculated to obtain the transmission scheduling strategy. The transmission priority reflects the importance and urgency of each data packet. High-priority data packets will be transmitted first to ensure the timely delivery of important data. The transmission scheduling strategy includes transmission timing and retransmission mechanism to ensure the transmission order and retransmission strategy of the data packet. The compressed data stream is packetized and encrypted to obtain the data packet to be transmitted. The packetization process divides the large data stream into small data packets for easy transmission and management. The encryption process ensures that the data is not stolen or tampered with during transmission, protecting the security of the data. The data packet to be transmitted is obtained through packetization and encryption. According to the transmission scheduling strategy, the transmission control parameters including the transmission timing and retransmission mechanism are generated. The transmission timing controls the sending order and time of the data packet, and the retransmission mechanism retransmits the data packet when it is lost to ensure data integrity and transmission reliability. The transmission control parameters provide guidance and control for the transmission process.

[0110] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0111] (1) Sort and check the integrity of the data packets to be transmitted according to the transmission control parameters to obtain an ordered sequence of data packets;

[0112] (2) decrypting and unpacking the ordered data packet sequence to obtain a compressed data stream;

[0113] (3) performing error detection and correction on the compressed data stream according to the error recovery information in the transmission control parameter to obtain a corrected data stream;

[0114] (4) performing entropy decoding on the modified data stream to obtain quantized data, and performing an inverse quantization operation according to the quantized data and a preset inverse quantization matrix to obtain frequency domain coefficients;

[0115] (5) performing inverse transformation on the frequency domain coefficients to obtain an initial reconstructed video frame, and performing segmentation processing on the initial reconstructed video frame according to the scene switching information in the transmission control parameter to obtain a segmented video frame;

[0116] (6) performing deblocking and de-ringing processing on the segmented video frames to obtain optimized video frames;

[0117] (7) According to the optimized video frame and adjacent frame information, motion compensation and interpolation processing are performed to obtain a video sequence with a smooth transition, and the video sequence with a smooth transition is converted and corrected to output a target video signal.

[0118] Specifically, the received data packets are sorted according to the transmission control parameters. The arrival order of these data packets may change during the transmission process due to network delay, packet loss, etc., so they need to be reordered according to the timestamp or sequence number in the transmission control parameters to restore the original transmission order. The integrity check verifies the integrity of each data packet through a checksum or hash function to ensure that the data packet has not been tampered with or damaged. Through these steps, an ordered and complete sequence of data packets is obtained. The ordered sequence of data packets is decrypted and unpacked to obtain a compressed data stream. Data packets are usually encrypted during transmission to protect the privacy and security of the data. The received data packets need to be decrypted. The decryption algorithm can be a symmetric encryption algorithm such as AES, or an asymmetric encryption algorithm such as RSA. The decrypted data packets need to be further unpacked to extract the actual compressed data stream. According to the error recovery information in the transmission control parameters, error detection and correction are performed on the compressed data stream to obtain a corrected data stream. Error detection and correction are achieved by forward error correction code (FEC) or cyclic redundancy check (CRC). FEC adds redundant information to the data stream so that the receiving end can detect and correct errors that occur during transmission. CRC detects errors in the data stream by calculating the checksum. Through these steps, the errors in the data stream are corrected to obtain the corrected data stream. The corrected data stream is entropy decoded to obtain quantized data. Entropy decoding is a key step in data decompression, which converts the compressed bit stream back to the original quantized data. Common entropy decoding methods include Huffman decoding and arithmetic decoding. Through entropy decoding, the quantized data before compression is restored. According to the quantized data and the preset inverse quantization matrix, the inverse quantization operation is performed to obtain the frequency domain coefficients. Quantization is part of data compression. By quantizing the data, the representation accuracy of the data is reduced, thereby reducing the amount of data. Inverse quantization is the inverse process of quantization. By using the inverse quantization matrix, the original frequency domain coefficients are restored. The formula is as follows:

[0119] D(i,j)=Q(i,j)×Z(i,j);

[0120] Among them, D(i,j) is the frequency domain coefficient after inverse quantization, Q(i,j) is the quantized data, and Z(i,j) is the corresponding element in the inverse quantization matrix. The frequency domain coefficient is inversely transformed to obtain the initial reconstructed video frame. Inverse transformation is the process of converting frequency domain data back to spatial domain data. Common methods include discrete cosine transform (IDCT) and discrete wavelet transform (IDWT). Through inverse transformation, the original video frame is reconstructed. According to the scene switching information in the transmission control parameter, the initial reconstructed video frame is segmented to obtain a segmented video frame. The scene switching information records the time point and area of ​​the scene change in the video signal. Based on this information, the initial reconstructed video frame is divided into several segments, each corresponding to a scene change area. Deblocking and de-ringing are performed on the segmented video frame to obtain an optimized video frame. Deblocking is to improve the image quality by eliminating the blocky artifacts generated during the video compression process. Common methods include low-pass filtering and adaptive filtering. De-ringing is to improve the visual quality of the image by reducing the ringing effect at the edge of the image. Through these processes, an optimized video frame is obtained. According to the optimized video frame and adjacent frame information, motion compensation and interpolation are performed to obtain a smooth transition video sequence. Motion compensation reduces motion blur by calculating the motion vector between adjacent frames, and interpolation smoothes the video sequence by generating transition frames. The formula is as follows:

[0121] P(x,y,t)=21-α5P2x-v " ,yv y ,t-15+αP(x,y,t);

[0122] Where P(x,y,t) is the pixel value at position (x,y) and time t, and v " and v y is the motion vector in the x and y directions, and α is the interpolation coefficient. Through motion compensation and interpolation processing, a smooth transition video sequence is obtained. The smooth transition video sequence is converted and corrected to output the target video signal. The conversion and correction process includes color space conversion, brightness and contrast adjustment, etc., to ensure that the final output video signal meets the requirements of the display device and the visual habits of the audience.

[0123] The above describes the multi-channel video signal automatic switching method based on the director switch in the embodiment of the present application. The following describes the multi-channel video signal automatic switching device based on the director switch in the embodiment of the present application. Figure 2 In an embodiment of the present application, an embodiment of a multi-channel video signal automatic switching device based on a director switch station includes:

[0124] The preprocessing module 201 is used to preprocess and calculate features of multiple video signal channels, generate a signal feature vector set and a signal quality index, and construct a corresponding dynamic weight matrix;

[0125] An optimization module 202, for performing adaptive frame rate control and energy optimization based on a signal feature vector set and a signal quality index, to obtain an optimized hardware working state and a preheating cache state;

[0126] The matching module 203 is used to extract and match multi-scale features of the video frames according to the optimized hardware working state and the preheating cache state, and generate a scene change map;

[0127] A fusion module 204 is used to perform intelligent switching decision and signal fusion based on scene change mapping and dynamic weight matrix to obtain a transition-processed video signal and a switching log;

[0128] The generating module 205 is used to perform data compression and transmission optimization on the video signal after the transition processing, and adjust the compression strategy according to the switching log to generate a compressed data stream and transmission control parameters;

[0129] The output module 206 is used to perform signal demultiplexing and reconstruction based on the compressed data stream and the transmission control parameters, and output the target video signal.

[0130] Through the synergy of the above components, by preprocessing and feature calculation of multiple video signal channels, generating signal feature vector sets and signal quality indicators, and performing adaptive frame rate control and energy optimization based on signal features and quality indicators, it is possible to effectively improve operating efficiency, reduce energy consumption, and ensure the quality of video processing. By extracting and matching multi-scale features of video frames and generating scene change mapping, it is possible to more accurately capture changes in video content. Based on scene change mapping and dynamic weight matrix, intelligent switching decisions and signal fusion can be performed to achieve smoother and more natural picture transitions and enhance the viewing experience. Data compression and transmission optimization of the video signal after transition processing, and adjusting the compression strategy according to the switching log, can improve transmission efficiency while ensuring video quality and reduce bandwidth pressure. Through the optimized signal demultiplexing and reconstruction process, the original video signal can be quickly and accurately restored to ensure the quality of the final output video. The present application realizes the automation of the entire process from video signal preprocessing to final output, greatly improving the efficiency of multi-channel video signal processing and switching, and reducing human intervention and possible errors.

[0131] The present application also provides a multi-channel video signal automatic switching device based on a director switchboard, and the multi-channel video signal automatic switching device based on a director switchboard includes a memory and a processor, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the multi-channel video signal automatic switching method based on the director switchboard in the above-mentioned embodiments.

[0132] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for automatic switching of multi-channel video signals based on a director switch.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0135] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-channel video signal automatic switching method based on a director switcher, characterized in that: The method comprises: Preprocess and calculate features of multiple video signal channels, generate signal feature vector sets and signal quality indicators, and construct corresponding dynamic weight matrices; Based on the signal feature vector set and the signal quality indicator, performing adaptive frame rate control and energy optimization to obtain an optimized hardware working state and a preheating cache state; According to the optimized hardware working state and the preheated cache state, multi-scale feature extraction and matching are performed on the video frame to generate a scene change map; Based on the scene change mapping and the dynamic weight matrix, intelligent switching decision and signal fusion are performed to obtain a transition-processed video signal and a switching log; Performing data compression and transmission optimization on the video signal after the transition processing, and adjusting the compression strategy according to the switching log to generate a compressed data stream and transmission control parameters; Signal demultiplexing and reconstruction are performed based on the compressed data stream and the transmission control parameters, and a target video signal is output.

2. The method for automatically switching multi-channel video signals based on a director switcher according to claim 1, characterized in that: The preprocessing and feature calculation of multiple video signal channels, generating a signal feature vector set and a signal quality index, and constructing a corresponding dynamic weight matrix include: Performing spatial domain filtering processing on each video signal channel to obtain a denoised video signal, and performing color correction on the denoised video signal to obtain a color-corrected video signal; Performing resolution standardization processing on the color-corrected video signal to obtain a standardized video signal, and performing texture feature extraction on the standardized video signal to obtain a texture feature map; Performing motion vector field calculation based on the texture feature map and the standardized video signal to obtain motion intensity features, and performing color histogram analysis and clustering on the standardized video signal to obtain color distribution features; Calculating the scene change frequency of the motion intensity feature and the color distribution feature to obtain a scene change feature; Performing discrete cosine transformation and quantization on the standardized video signal and calculating the signal-to-noise ratio and the structural similarity index to obtain a signal quality index; Constructing a multi-dimensional feature vector for the texture feature map, the motion intensity feature, the color distribution feature, and the scene change feature to obtain a signal feature vector set; The signal feature vector set and the signal quality index are subjected to principal component analysis and fuzzy comprehensive evaluation to obtain a dynamic weight matrix.

3. The method for automatically switching multi-channel video signals based on a director switcher according to claim 1, characterized in that: The method of performing adaptive frame rate control and energy optimization based on the signal feature vector set and the signal quality indicator to obtain an optimized hardware working state and a preheating cache state includes: Compressing the signal feature vector set to obtain a compressed feature vector, and constructing a frame rate prediction model and calculating a frame rate prediction value of each video signal channel according to the compressed feature vector and the signal quality index; Smoothing the frame rate prediction value to obtain a frame rate control parameter, and calculating the optimal operating frequency of the processor core according to the frame rate control parameter and the current hardware load to obtain a frequency adjustment instruction; Analyze the hardware resource usage information and predict future resource demand to obtain a resource demand prediction value, and optimize the switch strategy of the functional unit according to the resource demand prediction value and the frequency adjustment instruction to obtain a functional unit configuration plan; Analyze historical scene change data and identify typical scene patterns to obtain scene category labels, and generate a preheating data list based on the scene category labels and current scene features; Prioritize and calculate the optimal loading order of the elements in the preheating data list to obtain a cache preheating sequence; According to the cache preheating sequence and the functional unit configuration scheme, hardware resources are allocated to obtain an optimized hardware working state and a preheating cache state.

4. The method for automatically switching multi-channel video signals based on a director switcher according to claim 1, characterized in that: The step of extracting and matching multi-scale features of video frames according to the optimized hardware working state and the preheating cache state to generate a scene change map includes: According to the optimized hardware working state, the video frame is decomposed into multiple scales to obtain a multi-scale image sequence, and features are extracted for each scale image in the multi-scale image sequence to obtain a target point set and a descriptor set; According to the preheating cache state, predefined features are loaded from the cache, and the target point set is screened to obtain a screened target point set; Performing distribution optimization on the screened target point set to obtain a uniformly distributed feature point set, and constructing a feature database and generating a feature index structure based on the uniformly distributed feature point set and the descriptor set; Performing partition matching on feature points between consecutive frames according to the feature database and the feature index structure to obtain an initial matching result, and generating a geometric consistency matching set according to the initial matching result; Calculating motion information of the geometric consistency matching set to obtain a motion vector map, and performing region segmentation and analysis based on the motion vector map to obtain a scene change region; The scene change region is encoded and feature-fused to obtain a scene change map.

5. The method for automatically switching multi-channel video signals based on a director switcher according to claim 4, characterized in that: The intelligent switching decision and signal fusion are performed based on the scene change mapping and the dynamic weight matrix to obtain the video signal and the switching log after the transition processing, including: Performing a regional importance analysis on the scene change map to obtain a scene importance score, and calculating a comprehensive score of each video signal channel and generating a channel priority list based on the scene importance score and the dynamic weight matrix; Performing time-series smoothing processing on the channel priority list to obtain a switching candidate sequence, and generating an initial switching plan according to the switching candidate sequence and a preset program structure rule; Performing conflict detection and adjustment on the initial switching scheme to obtain an optimized switching scheme, and selecting a corresponding video signal channel and generating a source signal to be fused according to the optimized switching scheme; Performing spatiotemporal alignment processing on the source signal to be fused to obtain an aligned signal, and calculating an optimal transition effect parameter according to the aligned signal to obtain a transition effect instruction; The transition effect instruction is executed through the aligned signal to obtain a video signal after transition processing, and the optimized switching scheme and the execution process of the transition effect instruction are recorded to generate a switching log.

6. The method for automatically switching multi-channel video signals based on a director switcher according to claim 5, characterized in that: The step of compressing and optimizing the transmission of the video signal after the transition processing, adjusting the compression strategy according to the switching log, and generating a compressed data stream and transmission control parameters includes: Performing spatiotemporal analysis on the video signal after the transition processing to obtain a signal complexity map, and dividing the compression priority areas of the video frame according to the signal complexity map and the switching log to obtain a regional compression strategy; Performing dynamic quantization parameter matrix analysis on the video signal to obtain an initial quantization result, and refining and adjusting the initial quantization result according to the regional compression strategy to obtain an optimized quantization result; Performing entropy coding on the optimized quantization result to obtain an original data stream, and performing segmentation processing on the original data stream according to the scene switching information in the switching log to obtain a segmented data stream; Performing error recovery coding on the segmented data stream to obtain a compressed data stream, and calculating the transmission priority of the data packet according to the network status and the switching log to obtain a transmission scheduling strategy; The compressed data stream is packetized and encrypted to obtain data packets to be transmitted, and transmission control parameters including transmission timing and retransmission mechanism are generated according to the transmission scheduling strategy.

7. The method for automatically switching multi-channel video signals based on a director switcher according to claim 6, characterized in that: The method of performing signal demultiplexing and reconstruction based on the compressed data stream and the transmission control parameter and outputting a target video signal comprises: Sorting and integrity checking the data packets to be transmitted according to the transmission control parameters to obtain an ordered sequence of data packets; Decrypting and unpacking the ordered data packet sequence to obtain the compressed data stream; performing error detection and correction on the compressed data stream according to the error recovery information in the transmission control parameter to obtain a corrected data stream; Performing entropy decoding on the modified data stream to obtain quantized data, and performing an inverse quantization operation according to the quantized data and a preset inverse quantization matrix to obtain frequency domain coefficients; Performing an inverse transformation on the frequency domain coefficients to obtain an initial reconstructed video frame, and performing segmentation processing on the initial reconstructed video frame according to the scene switching information in the transmission control parameter to obtain segmented video frames; Performing deblocking and de-ringing processing on the segmented video frames to obtain optimized video frames; According to the optimized video frame and adjacent frame information, motion compensation and interpolation processing are performed to obtain a video sequence with a smooth transition, and the video sequence with a smooth transition is converted and corrected to output a target video signal.

8. A multi-channel video signal automatic switching device based on a director switcher, characterized in that: The device is used to execute the multi-channel video signal automatic switching method based on the director switcher according to any one of claims 1 to 7, and comprises: A preprocessing module is used to preprocess and calculate features of multiple video signal channels, generate signal feature vector sets and signal quality indicators, and construct corresponding dynamic weight matrices; An optimization module, configured to perform adaptive frame rate control and energy optimization based on the signal feature vector set and the signal quality index, to obtain an optimized hardware working state and a preheating cache state; A matching module, configured to extract and match multi-scale features of video frames according to the optimized hardware working state and the preheated cache state, and generate a scene change map; A fusion module, used to perform intelligent switching decision and signal fusion based on the scene change mapping and the dynamic weight matrix to obtain a transition-processed video signal and a switching log; A generating module, used for performing data compression and transmission optimization on the video signal after the transition processing, adjusting the compression strategy according to the switching log, and generating a compressed data stream and transmission control parameters; The output module is used to perform signal demultiplexing and reconstruction based on the compressed data stream and the transmission control parameters, and output a target video signal.

9. A multi-channel video signal automatic switching device based on a director switcher, characterized in that: The multi-channel video signal automatic switching device based on the director switcher includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory so that the multi-channel video signal automatic switching device based on the director switcher executes the multi-channel video signal automatic switching method based on the director switcher as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the multi-channel video signal automatic switching method based on the director switcher as described in any one of claims 1-7 is implemented.

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