Streaming media platform optimization method and system based on video image processing

Abstract

The present invention discloses a streaming media platform optimization method and system based on video image processing, which relates to the field of streaming media technology. The method comprises collecting an image frame sequence, extracting brightness histogram eigenvalues ​​and color saturation data, calculating a quality score of the image frame sequence, and establishing a dynamic bit rate allocation model; encoding and compressing the image frame sequence through the dynamic bit rate allocation model to generate a video data stream with an adaptive bit rate, and transmitting the stream to a streaming media server; collecting playback quality feedback data from the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data. The present invention extracts brightness histogram and color saturation data of video frames, establishes a quality scoring model based on a dual-branch neural network, and combines dynamic bit rate allocation to achieve adaptive video coding compression, thereby effectively reducing bandwidth occupancy while ensuring video quality, thereby improving the user experience and system performance of streaming media services.

Description

Technical Field

[0001] The present invention relates to the technical field of streaming media, and in particular to a streaming media platform optimization method and system based on video image processing. Background Art

[0002] Traditional streaming media transmission technologies primarily employ fixed bitrates or adaptive bitrate adjustment schemes with preset thresholds, dynamically adjusting the video bitrate by monitoring network bandwidth conditions. However, these schemes often overlook the inherent differences in the characteristics of the video content itself and fail to fully consider the impact of visual quality factors such as image complexity, brightness distribution, and color saturation on transmission performance. Furthermore, existing technologies lack a real-time feedback mechanism for the user experience on the playback end during the bitrate adaptive adjustment process, making it difficult to accurately evaluate and optimize the actual playback effect, which can easily lead to problems such as unstable image quality and playback lag.

[0003] The TCP-based adaptive bitrate streaming (ABR) technology currently widely used in the industry, while achieving dynamic bitrate adjustment to a certain extent, its decision-making mechanism is overly dependent on network status parameters and fails to establish an effective mapping relationship between video content characteristics and transmission strategies. Especially in complex and changing network environments, due to the lack of accurate assessment and intelligent prediction capabilities of video image quality, existing technologies have difficulty achieving optimal bitrate allocation under bandwidth constraints. In addition, traditional solutions generally use fixed compression parameters and encoding modes during the video encoding process, failing to flexibly adjust the compression strategy based on the image characteristics of different scenes. This not only affects the efficient utilization of bandwidth resources, but also makes it difficult to ensure the continuity of video playback and the fidelity of image details. In actual applications, these technical limitations often lead to significant fluctuations in user experience quality, hindering the further improvement of the service performance and user satisfaction of streaming media platforms. Summary of the Invention

[0004] In view of the many shortcomings of existing streaming media transmission technology in video quality assessment, bit rate adaptive adjustment and user experience optimization, especially the lack of in-depth analysis of video content characteristics and intelligent transmission control mechanism, which makes it impossible to achieve optimal transmission effect in complex network environments, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to establish an adaptive optimization mechanism based on video content features and playback quality feedback through video image processing and machine learning technology to improve the transmission efficiency and user experience quality of the streaming media platform.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a streaming media platform optimization method based on video image processing, which includes collecting an image frame sequence in a target video stream, and extracting brightness histogram eigenvalues ​​and color saturation data from the image frame sequence; calculating the quality score of the image frame sequence based on the brightness histogram eigenvalues ​​and the color saturation data, and establishing a dynamic bit rate allocation model; encoding and compressing the image frame sequence through the dynamic bit rate allocation model to generate an adaptive bit rate video data stream, which is transmitted to a streaming media server; collecting playback quality feedback data of the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data.

[0008] As a preferred solution of the streaming media platform optimization method based on video image processing described in the present invention, it includes: collecting the playback quality feedback data of the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data, including: obtaining the playback quality feedback data of the streaming media server, wherein the playback quality feedback data includes playback fluency, buffer status and delay parameters; constructing a playback quality evaluation system, performing weighted calculation on the playback quality feedback data, generating a standardized playback quality score, and comprehensively evaluating the playback effect; establishing a feedback optimization mechanism based on the playback quality score, optimizing and adjusting the parameters of the dynamic bit rate allocation model; and feeding back the optimized parameters to the dynamic bit rate allocation model in real time to optimize the transmission quality of the video data stream.

[0009] As a preferred solution of the streaming media platform optimization method based on video image processing described in the present invention, the method in which the dynamic bit rate allocation model encodes and compresses the image frame sequence is as follows: based on the target bit rate output by the dynamic bit rate allocation model, the image frame sequence is divided into image groups; according to the frames of the image group, differential compression method is used for compression, and each frame of the image is divided into macroblock units using a rate-distortion optimization strategy; for the encoding mode selection of the macroblock unit, an adaptive cost function is introduced to generate a video data stream with an adaptive bit rate; the video data stream is encapsulated, and the encapsulated video data stream is transmitted to the streaming media server via the RTMP protocol.

[0010] As a preferred solution of the streaming media platform optimization method based on video image processing described in the present invention, the method for constructing the dynamic bitrate allocation model is as follows: based on the brightness histogram eigenvalues ​​and color saturation data, a dual-branch neural network structure is used to establish an image quality assessment model; the features extracted by the dual-branch neural network structure are weightedly fused to obtain a quality score of the image frame sequence; based on the temporal variation law of the quality score, a sliding time window method is used to establish a dynamic bitrate allocation model.

[0011] As a preferred solution of the streaming media platform optimization method based on video image processing described in the present invention, wherein: the dual-branch neural network structure includes a first branch neural network structure and a second branch neural network structure; the first branch neural network structure receives the brightness histogram feature value and extracts the brightness distribution feature through a three-layer convolution layer; the second branch neural network structure receives the color saturation data and extracts the color feature through a fully connected layer; the dynamic bitrate allocation model includes a quality threshold judgment module and a bitrate adjustment module; the quality threshold judgment module sets an adaptive adjustment threshold according to the fluctuation amplitude of the quality score; the bitrate adjustment module dynamically calculates the target bitrate based on the adaptive adjustment threshold.

[0012] As a preferred solution of the streaming media platform optimization method based on video image processing of the present invention, the specific formula of the dynamic bit rate allocation model is as follows:

[0013]

[0014] Q(t)=1+β·tanh(Δq / σ)

[0015]

[0016] Among them, R(t) is the target bit rate value at time t, R b is the base bit rate, T is the time window length, α is the time decay coefficient, Q(t) is the quality score function, ΔH i is the image entropy change value of the i-th time window, n is the number of sampling windows, is the visual feature response function, β is the quality adjustment coefficient, Δq is the quality score deviation, σ is the quality score standard deviation, v is the normalized image complexity characteristic value, λ is the response function gain coefficient, and μ is the response function threshold.

[0017] As a preferred solution of the streaming media platform optimization method based on video image processing described in the present invention, wherein: an image frame sequence in a target video stream is collected, and brightness histogram eigenvalues ​​and color saturation data are extracted from the image frame sequence, including: collecting an image frame sequence in a target video stream, sampling the image frame sequence frame by frame according to a preset sampling interval, and obtaining a key image frame; converting the key image frame from an RGB color space to an HSV color space, and extracting the brightness component in the HSV color space; counting the pixel value distribution of the brightness component, establishing a brightness histogram, and calculating the brightness histogram eigenvalues; based on the saturation component in the HSV space, analyzing the color space distribution characteristics, and obtaining regional saturation characteristics and color saturation data.

[0018] In the second aspect, an embodiment of the present invention provides a streaming media platform optimization system based on video image processing, which includes: an extraction module for collecting image frame sequences in a target video stream and extracting brightness histogram eigenvalues ​​and color saturation data from the image frame sequence; an establishment module for calculating the quality score of the image frame sequence based on the brightness histogram eigenvalues ​​and the color saturation data, and establishing a dynamic bit rate allocation model; a generation module for encoding and compressing the image frame sequence through the dynamic bit rate allocation model, generating a video data stream with an adaptive bit rate, and transmitting it to a streaming media server; an optimization module for collecting playback quality feedback data of the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data.

[0019] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the streaming media platform optimization method based on video image processing as described in the first aspect of the present invention are implemented.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the streaming media platform optimization method based on video image processing as described in the first aspect of the present invention are implemented.

[0021] The beneficial effects of the present invention are as follows: the present invention extracts the brightness histogram and color saturation data of video frames, establishes a quality scoring model based on a dual-branch neural network, and combines it with dynamic bitrate allocation to achieve adaptive video coding compression; the system introduces a closed-loop optimization mechanism based on playback quality feedback, continuously adjusts the bitrate allocation strategy, effectively reduces bandwidth occupancy while ensuring video quality, and improves the user experience and system performance of streaming media services; especially in network fluctuations and complex video content scenarios, it shows good adaptability and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0023] Figure 1 This is a flowchart of the streaming media platform optimization method based on video image processing in Example 1.

[0024] Figure 2This is a brightness histogram of the streaming media platform optimization method based on video image processing in Example 1.

[0025] Figure 3 This is a comparison chart of video quality scores of the streaming media platform optimization method based on video image processing in Example 2.

[0026] Figure 4 This is a system delay comparison chart of the streaming media platform optimization method based on video image processing in Example 2. DETAILED DESCRIPTION

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0029] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0030] Example 1

[0031] Reference Figures 1 and 2 , which is the first embodiment of the present invention, provides a streaming media platform optimization method based on video image processing, including:

[0032] S1: Acquire an image frame sequence in a target video stream, and extract brightness histogram feature values ​​and color saturation data from the image frame sequence.

[0033] Specifically, an image frame sequence in a target video stream is collected, and the image frame sequence is sampled frame by frame according to a preset sampling interval to obtain a key image frame; the key image frame is converted from RGB color space to HSV color space, and the brightness component in the HSV color space is extracted.

[0034] It should be noted that the principle of converting the key image frame from RGB color space to HSV color space is: RGB color space is an additive color model based on the three components of red (R), green (G) and blue (B); HSV color space is based on human perception and consists of hue (Hue), saturation (Saturation) and brightness value (Value). The conversion process requires nonlinear mapping.

[0035] Furthermore, the conversion processing steps are as follows: normalize the RGB image data of the input key image frame and uniformly map the value ranges of the three RGB components to the interval [0,1]; compare the sizes of the three components R, G and B, and take the maximum value as the brightness component V in the HSV space; calculate the saturation S: find the maximum and minimum values ​​of R, G and B; when the maximum value of R, G and B is not 0, the saturation S is equal to the difference between the maximum and minimum values ​​divided by the maximum value; when the maximum value of R, G and B is 0, the saturation S is directly set to 0.

[0036] Furthermore, the hue H is calculated: when the maximum value among R, G and B is equal to the minimum value, the hue H is set to 0; if R is the maximum value, the hue H is equal to 60 degrees multiplied by G minus B divided by the difference between the maximum and minimum values ​​of R, G and B; if G is the maximum value, the hue H is equal to 60 degrees multiplied by (2 plus B minus R divided by the difference between the maximum and minimum values ​​of R, G and B); if B is the maximum value, the hue H is equal to 60 degrees multiplied by (4 plus R minus G divided by the difference between the maximum and minimum values ​​of R, G and B); if the calculated hue H is a negative value, add 360 to normalize it to the range of [0,360].

[0037] Specifically, extract the brightness component: extract the calculated brightness value V separately, for 8-bit images, remap the brightness value range to the [0, 255] interval, and save the extracted brightness component data for subsequent histogram feature analysis.

[0038] Furthermore, the pixel value distribution of the brightness component is statistically analyzed to establish a brightness histogram and calculate the brightness histogram eigenvalue. The specific formula is as follows:

[0039]

[0040] Where P(k) is the probability of occurrence of a pixel with brightness value k, H(k) is the number of pixels with brightness value k, M is the number of pixels of image height, N is the number of pixels of image width, a is the mean brightness, reflecting the overall brightness level of the image, b is the standard deviation, indicating the degree of discreteness of the brightness distribution, and k is the brightness level value, ranging from [0, 255].

[0041] Furthermore, based on the saturation component in the HSV space, the color space distribution characteristics are analyzed to obtain regional saturation characteristics and color saturation data.

[0042] It should be noted that the mean and standard deviation of the brightness histogram are used as the brightness histogram feature values; the mean and variance indicators of the color saturation of the entire image are used as color saturation data.

[0043] S2: Calculate the quality score of the image frame sequence according to the brightness histogram feature value and the color saturation data, and establish a dynamic bit rate allocation model.

[0044] Specifically, the method for constructing the dynamic bit rate allocation model is to establish an image quality assessment model using a dual-branch neural network structure based on the brightness histogram eigenvalues ​​and color saturation data.

[0045] It should be noted that the dual-branch neural network structure includes a first-branch neural network structure and a second-branch neural network structure; the first-branch neural network structure receives the brightness histogram feature value and extracts the brightness distribution feature through three convolutional layers; the second-branch neural network structure receives the color saturation data and extracts the color feature through the fully connected layer.

[0046] Furthermore, the features extracted by the dual-branch neural network structure are weightedly fused to obtain the quality score of the image frame sequence.

[0047] It should be noted that the basic parameters of the dynamic bitrate allocation model initialized based on the quality score include: the base bitrate range is [4Mbps, 12Mbps], the sliding time window length is set to 30 frames; the quality score value range is [0, 100], and the bitrate adjustment step is set to 10% of the base bitrate.

[0048] Furthermore, a tiered bitrate allocation strategy is established. When the quality score is greater than 80, the target bitrate is set to 1.2 times the baseline bitrate; when the quality score is between 50 and 80, the baseline bitrate is kept unchanged; when the quality score is less than 50, the target bitrate is reduced to 0.8 times the baseline bitrate; a bitrate smoothing transition mechanism is designed. When the bitrate variation of adjacent time windows exceeds 20% of the current bitrate, a linear interpolation algorithm is activated for smoothing, and the bitrate adjustment value is stored in a buffer with a capacity of 5 frames; based on the bitrate adjustment value in the buffer, it is gradually released at a fixed time interval of 100ms to achieve a smooth bitrate transition and avoid drastic fluctuations in picture quality.

[0049] Specifically, based on the temporal variation of the quality score, a sliding time window method is used to establish a dynamic bit rate allocation model. The specific formula is as follows:

[0050]

[0051] Q(t)=1+β·tanh(Δq / σ)

[0052]

[0053] Among them, R(t) is the target bit rate value at time t, R b is the base bit rate, T is the time window length, α is the time decay coefficient, Q(t) is the quality score function, ΔH i is the image entropy change value of the i-th time window, n is the number of sampling windows, is the visual feature response function, β is the quality adjustment coefficient, Δq is the quality score deviation, σ is the quality score standard deviation, v is the normalized image complexity characteristic value, λ is the response function gain coefficient, and μ is the response function threshold.

[0054] It should be noted that the dynamic bitrate allocation model includes a quality threshold judgment module and a bitrate adjustment module; the quality threshold judgment module sets an adaptive adjustment threshold according to the fluctuation range of the quality score; and the bitrate adjustment module dynamically calculates the target bitrate based on the adaptive adjustment threshold.

[0055] Furthermore, the quality threshold judgment module: when the fluctuation range of the quality score continues to exceed the preset threshold, the quality threshold judgment module will automatically increase the adaptive adjustment threshold; when the quality scores of multiple consecutive time windows show a downward trend, the system will lower the adaptive adjustment threshold; when the image entropy change value ΔH i When a sudden change occurs, the system will temporarily adjust the adaptive adjustment threshold; when the quality score standard deviation σ is large, it indicates that the picture quality is unstable, and the system will expand the adjustment range of the adaptive adjustment threshold.

[0056] Furthermore, the rate adjustment module: when the quality score deviation Δq exceeds the adaptive adjustment threshold, the rate adjustment module will trigger the rate adjustment and calculate the new target rate value R(t); when the image complexity feature value v increases rapidly, the system will increase the baseline rate R b weight; when the output value of the visual feature response function μ is close to saturation, the system will limit the growth of the target bit rate; when the quality score within the time window is stable, the system will reduce the influence of the time decay coefficient λ and maintain the current bit rate allocation strategy.

[0057] S3: Encoding and compressing the image frame sequence using the dynamic bit rate allocation model to generate a video data stream with an adaptive bit rate, and transmitting the stream to a streaming media server.

[0058] Specifically, the method for encoding and compressing the image frame sequence using the dynamic bit rate allocation model is to divide the image frame sequence into image groups based on the target bit rate output by the dynamic bit rate allocation model.

[0059] It should be noted that the GOP sets the GOP length to 60 frames, where each GOP contains 1 I-frame, 14 P-frames and 45 B-frames, and sets the maximum number of reference frames to 4.

[0060] Furthermore, differential compression is used according to the frames of the image group, and a rate-distortion optimization strategy is used to divide each frame of the image into macroblock units; an adaptive cost function is introduced to select the encoding mode of the macroblock unit to generate a video data stream with an adaptive bit rate; the video data stream is encapsulated and transmitted to the streaming media server through the RTMP protocol.

[0061] It should be noted that the macroblock coding strategy divides each frame of image into 16×16 pixel macroblock units; the formula of the adaptive cost function is: J=D+cB, where D is the distortion, B is the number of bits, and c is the Lagrange multiplier.

[0062] S4: Collecting playback quality feedback data of the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data.

[0063] Specifically, playback quality feedback data from the streaming media server is obtained, where the playback quality feedback data includes playback fluency, buffer status, and delay parameters.

[0064] Furthermore, a playback quality evaluation system is constructed to perform weighted calculations on playback quality feedback data, generate standardized playback quality scores, and comprehensively evaluate playback effects;

[0065] It should be noted that the weighted calculation is: setting the frame rate weight to 0.3, the buffer occupancy weight to 0.2, the playback delay weight to 0.2, the audio and video synchronization weight to 0.15, and the number of freezes weight to 0.15 to generate a standardized playback quality score.

[0066] Furthermore, a feedback optimization mechanism is established based on the playback quality score to optimize and adjust the parameters of the dynamic bitrate allocation model.

[0067] It should be noted that the feedback optimization mechanism is: when the playback quality score is lower than 75 points, the benchmark bitrate in the dynamic bitrate allocation model will be reduced by 15%; when the playback quality score is between 75-85 points, the current parameters will remain unchanged; when the playback quality score is higher than 85 points and the network bandwidth is sufficient, the benchmark bitrate will be increased, and the increase will not exceed 10% of the current bitrate.

[0068] Specifically, the optimized parameters are fed back to the dynamic bit rate allocation model in real time to optimize the transmission quality of the video data stream.

[0069] Furthermore, this embodiment also provides a streaming media platform optimization system based on video image processing, including: an extraction module for collecting image frame sequences in the target video stream and extracting brightness histogram eigenvalues ​​and color saturation data from the image frame sequence; an establishment module for calculating the quality score of the image frame sequence based on the brightness histogram eigenvalues ​​and the color saturation data, and establishing a dynamic bit rate allocation model; a generation module for encoding and compressing the image frame sequence through the dynamic bit rate allocation model, generating a video data stream with an adaptive bit rate, and transmitting it to a streaming media server; an optimization module for collecting playback quality feedback data of the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data.

[0070] This embodiment also provides a computer device, which is suitable for the case of a streaming media platform optimization method based on video image processing, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the streaming media platform optimization method based on video image processing proposed in the above embodiment.

[0071] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0072] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps: acquiring an image frame sequence in a target video stream, and extracting brightness histogram feature values ​​and color saturation data from the image frame sequence; calculating a quality score of the image frame sequence based on the brightness histogram feature values ​​and the color saturation data, and establishing a dynamic bit rate allocation model; encoding and compressing the image frame sequence through the dynamic bit rate allocation model to generate a video data stream with an adaptive bit rate, and transmitting the stream to a streaming media server; acquiring playback quality feedback data from the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data.

[0073] In summary, the present invention extracts the brightness histogram and color saturation data of video frames, establishes a quality scoring model based on a dual-branch neural network, and combines it with dynamic bitrate allocation to achieve adaptive video coding compression; the system introduces a closed-loop optimization mechanism based on playback quality feedback, continuously adjusts the bitrate allocation strategy, effectively reduces bandwidth occupancy while ensuring video quality, and improves the user experience and system performance of streaming media services; especially in network fluctuations and complex video content scenarios, it shows good adaptability and stability.

[0074] Example 2

[0075] Reference Figures 3 and 4 , which is the second embodiment of the present invention, provides a streaming media platform optimization method based on video image processing. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0076] Specifically, we selected a 4K resolution (3840×2160) test video sequence, encompassing four typical scenarios: sports events, live news broadcasts, online education, and live gaming. Each scenario was 30 minutes long. The test environment utilized a server equipped with an Intel i9-12900K processor and 64GB of RAM, connected via Gigabit Ethernet.

[0077] Furthermore, the test video sequence was frame sampled with a sampling interval of 0.5 seconds to ensure that key moments of scene changes were captured. The OpenCV library was used to convert the RGB image to the HSV color space. The V channel data was extracted to construct a 256-level brightness histogram. The mean, variance, skewness, and kurtosis of the histogram were calculated as brightness feature values. Simultaneously, the S channel data was extracted to calculate the regional saturation mean and standard deviation as color saturation features.

[0078] Furthermore, in the dual-branch neural network architecture, the first branch uses a three-layer convolutional structure (with kernel sizes of 7×7, 5×5, and 3×3, respectively) to process brightness features, while the second branch uses three fully connected layers (with node numbers of 1024, 512, and 256, respectively) to process color features. The features of the two branches are combined through a weighted fusion layer, and the weight coefficients are optimized using the backpropagation algorithm.

[0079] Specifically, the dynamic bitrate allocation model uses a 60-second sliding time window with a window sliding step of 1 second. The initial value of the time decay coefficient λ is set to 0.95, the quality adjustment coefficient β is set to 0.8, the response function gain coefficient α is 1.2, and the response function threshold θ is 0.85. The baseline bit rate is set in the range of 6-15Mbps according to different scene types. To verify the system performance, comparative tests are conducted using the traditional method (fixed bit rate CBR) and the present invention. The system records the performance indicators of the video quality score (VQS) and the system end-to-end delay of each solution.

[0080] Further, such as Figure 3 As shown in the video quality score comparison chart, the score of the proposed method consistently remains between 90 and 98 points, with a stable curve and small fluctuations (approximately ±3 points), indicating that the method can continuously provide high-quality video output. In contrast, the scores of traditional methods generally fluctuate between 80 and 90 points, with large fluctuations (approximately ±5 points), indicating that traditional methods have obvious shortcomings in maintaining video quality stability.

[0081] Furthermore, Figure 4 As shown in the system delay comparison chart, the end-to-end delay of the method of the present invention is stably maintained at around 400 milliseconds, with a fluctuation range of approximately ±50 milliseconds, demonstrating excellent system responsiveness. In contrast, the system delay of traditional methods is significantly higher, with an average delay of 800 milliseconds and large fluctuations (approximately ±100 milliseconds). This high delay can significantly reduce the user experience, especially limiting its application in real-time interactive scenarios.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A streaming media platform optimization method based on video image processing, characterized by: include, Acquire an image frame sequence in a target video stream, and extract brightness histogram feature values ​​and color saturation data from the image frame sequence; Calculating a quality score of an image frame sequence based on the brightness histogram feature value and the color saturation data, and establishing a dynamic bit rate allocation model; The image frame sequence is encoded and compressed using the dynamic bit rate allocation model to generate a video data stream with an adaptive bit rate, which is then transmitted to a streaming media server; Collecting playback quality feedback data of the streaming media server, and optimizing and adjusting the dynamic bit rate allocation model based on the playback quality feedback data; Dividing the image frame sequence into image groups based on a target bit rate output by the dynamic bit rate allocation model; According to the frames of the image group, a differential compression method is used for compression, and each frame of the image is divided into macroblock units using a rate-distortion optimization strategy; Selecting a coding mode for the macroblock unit, introducing an adaptive cost function, and generating a video data stream with an adaptive bit rate; Encapsulating the video data stream, and transmitting the encapsulated video data stream to a streaming media server via the RTMP protocol; The method for constructing the dynamic bit rate allocation model is as follows: According to the brightness histogram eigenvalue and color saturation data, a dual-branch neural network structure is used to establish an image quality assessment model; The features extracted by the dual-branch neural network structure are weighted and fused to obtain the quality score of the image frame sequence; Based on the temporal variation of the quality score, a dynamic bit rate allocation model is established using a sliding time window method; The specific formula of the dynamic bit rate allocation model is as follows: in, is the target bit rate value at time t, is the base bit rate, is the time window length, is the time attenuation coefficient, is the quality scoring function, is the image entropy change value of the i-th time window, n is the number of sampling windows, is the visual feature response function, is the quality adjustment coefficient, is the quality score deviation, is the standard deviation of the quality score, is the normalized image complexity eigenvalue, is the response function gain coefficient, is the response function threshold.

2. The method for optimizing a streaming media platform based on video image processing according to claim 1, wherein: Collecting playback quality feedback data from the streaming media server, and optimizing and adjusting the dynamic bitrate allocation model based on the playback quality feedback data, including: Obtaining playback quality feedback data from the streaming media server, wherein the playback quality feedback data includes playback smoothness, buffering status, and delay parameters; Constructing a playback quality evaluation system to perform weighted calculations on the playback quality feedback data, generate standardized playback quality scores, and comprehensively evaluate playback effects; Establishing a feedback optimization mechanism based on the playback quality score to optimize and adjust parameters of a dynamic bitrate allocation model; The optimized parameters are fed back to the dynamic bit rate allocation model in real time to optimize the transmission quality of the video data stream.

3. The method for optimizing a streaming media platform based on video image processing according to claim 1, wherein: The dual-branch neural network structure includes a first-branch neural network structure and a second-branch neural network structure; the first-branch neural network structure receives the brightness histogram feature value and extracts brightness distribution features through a three-layer convolution layer; the second-branch neural network structure receives the color saturation data and extracts color features through a fully connected layer; The dynamic bitrate allocation model includes a quality threshold judgment module and a bitrate adjustment module; the quality threshold judgment module sets an adaptive adjustment threshold according to the fluctuation range of the quality score; and the bitrate adjustment module dynamically calculates the target bitrate based on the adaptive adjustment threshold.

4. The method for optimizing a streaming media platform based on video image processing according to claim 3, wherein: Acquiring an image frame sequence in a target video stream and extracting brightness histogram feature values ​​and color saturation data from the image frame sequence, including: Acquire an image frame sequence in a target video stream, sample the image frame sequence frame by frame according to a preset sampling interval, and obtain a key image frame; Converting the key image frame from an RGB color space to an HSV color space, and extracting a brightness component in the HSV color space; Counting the pixel value distribution of the brightness component, establishing a brightness histogram, and calculating the brightness histogram eigenvalue; Based on the saturation component in the HSV space, the color space distribution characteristics are analyzed to obtain regional saturation characteristics and color saturation data.

5. A streaming media platform optimization system based on video image processing, based on the streaming media platform optimization method based on video image processing according to any one of claims 1 to 4, characterized in that: include, An extraction module, configured to collect an image frame sequence in a target video stream and extract brightness histogram feature values ​​and color saturation data from the image frame sequence; Establishing a module for calculating a quality score of an image frame sequence based on the brightness histogram feature value and the color saturation data, and establishing a dynamic bit rate allocation model; A generation module is used to perform encoding and compression processing on the image frame sequence through the dynamic bit rate allocation model to generate a video data stream with an adaptive bit rate and transmit it to a streaming media server; The optimization module is used to collect playback quality feedback data of the streaming media server and optimize and adjust the dynamic bit rate allocation model based on the playback quality feedback data.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the streaming media platform optimization method based on video image processing according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the streaming media platform optimization method based on video image processing according to any one of claims 1 to 4 are implemented.

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