Advertisement video material putting effect evaluation method and device, equipment and storage medium

Abstract

The invention relates to the technical field of image processing, solves the problems that in the prior art, an advertisement piece cannot be associated with an original material, and the accuracy of evaluating the putting effect of the material in different scenes is low, and provides an advertisement video material putting effect evaluation method and device, equipment and a storage medium. The method comprises the following steps: acquiring at least one original video material uploaded by a target object in an advertisement putting scene; performing scene switching detection on the original video material, and segmenting the original video material into a plurality of scene video clips; embedding preset blind watermark information into each frame of image in each scene video clip based on frequency domain transformation to obtain a target video collection; and performing blind watermark information extraction on the target video collection, determining material fragment information, evaluating each material fragment according to the delivery data information and the material fragment information when the advertisement is delivered, and determining a delivery effect evaluation result. According to the invention, accurate material performance data is provided, and an advertiser is helped to optimize material use.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method, device, equipment and storage medium for evaluating the effect of advertising video material delivery. Background Art

[0002] With the rapid development of the digital advertising market, the management of advertising materials and the analysis of advertising effect have become important links in the advertising industry. Existing advertising material management technologies mainly include the storage, classification and retrieval of materials, while the delivery effect analysis technology focuses on the data collection and basic analysis after the advertisement is delivered. These technologies usually rely on infrastructure such as cloud storage services, content management systems (CMS) and advertising servers. However, existing technologies have the following limitations: material management usually relies on manual operations, which can easily lead to the reuse of materials, confusion and loss of versions, and weak correlation between the finished advertisement and the original material, resulting in the inability to accurately track the use effect of the material; the monitoring and analysis of advertising effects mostly rely on overall data and lack of detailed analysis of specific material performance.

[0003] The existing Chinese patent CN104954819A discloses a method for realizing intelligent monitoring of new media advertisements using digital watermark coding technology. The method includes: firstly, the new media advertisements to be broadcast are classified and managed by the advertising material management module, and then the digital watermark coding module performs watermark insertion processing on the new media advertisements to be broadcast, and performs unique coding identification, and the terminal device transmits the results of user terminal analysis (containing the unique code of the digital watermark) back to the national advertising material library through the Internet or the two-way return network of the radio and television network operator, and then, by comparing the collected advertising analysis data of the user terminal with the front-end advertising material library, the advertising monitoring data such as the new media advertising content watched by the user and the frequency of exposure of specific advertisements are obtained. Although the above patent discloses a technical solution for obtaining advertising monitoring data by comparing advertising analysis data with a material library, the correlation between the finished advertisement and the original material is not strong. Once the material is used for multiple finished films or delivered on different platforms, it is difficult to track its specific performance and contribution, which limits the improvement and optimization of material utilization. In addition, there is a lack of detailed analysis of the performance of specific materials, which makes it impossible to accurately evaluate the effects of the materials in different scenarios, making it difficult to implement targeted optimization strategies.

[0004] Therefore, how to associate the finished advertisement with the original material and accurately evaluate the effect of the material in different scenarios is an urgent problem to be solved. Summary of the invention

[0005] In view of this, the present invention provides a method, device, equipment and storage medium for evaluating the effect of advertising video material delivery, so as to solve the problem in the prior art that the advertising film cannot be associated with the original material and the accuracy of evaluating the delivery effect of the material in different scenarios is low.

[0006] The technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention provides a method for evaluating the effect of advertising video material delivery, the method comprising:

[0008] S1: Obtain at least one original video material uploaded by the target object in the advertising delivery scenario;

[0009] S2: performing scene switching detection on the original video material, and dividing the original video material into a plurality of scene video segments according to the scene switching detection result;

[0010] S3: Using the image processing technology based on frequency domain transformation, the preset blind watermark information is embedded into each frame image in each scene video clip, and the video clips of each scene after the blind watermark information is embedded are edited to obtain the target video highlights;

[0011] S4: extracting blind watermark information from the target video collection, and determining material segment information based on the extracted blind watermark information, wherein the material segment information at least includes the name and position of the original video material used in the target video collection;

[0012] S5: When the target video collection is used for advertisement delivery, each material segment is evaluated based on the pre-collected delivery data information and the material segment information, and all material evaluation results are summarized to obtain a delivery effect evaluation result.

[0013] Preferably, the S2 includes:

[0014] S21: Decomposing the original video material into multiple frames of original video images, and obtaining a color histogram corresponding to each original video image;

[0015] S22: performing difference calculation on the color histograms of the original video images of adjacent frames to obtain a color histogram difference value;

[0016] S23: If the color histogram difference value is greater than a preset color histogram threshold, the adjacent frames of original video images are classified into two scene video segments.

[0017] Preferably, the preset color histogram threshold is determined by the following steps:

[0018] S2301: Acquire a current frame image from adjacent frames of original video images;

[0019] S2302: Determine a color histogram set of all frame images before the current frame image according to the current frame image;

[0020] S2303: Determine the color histogram threshold according to the color histogram set in combination with a preset smoothing parameter and an initial threshold.

[0021] Preferably, S3 includes:

[0022] S31: performing color space conversion on each frame image in each scene video clip to obtain a target color channel;

[0023] S32: performing discrete cosine transform on the target color channel to convert each frame image in each scene video clip into a scene frequency domain image;

[0024] S33: modifying the high-frequency region in the scene frequency domain image according to the preset key, and embedding the preset blind watermark information into the scene frequency domain image.

[0025] Preferably, the S33 includes:

[0026] S331: Obtain the size of the template of the area to be embedded according to the preset blind watermark information;

[0027] S332: selecting any target area in the scene frequency domain image according to the size of the template of the area to be embedded, wherein the size of the target area is the same as the size of the template of the area to be embedded;

[0028] S333: Perform frequency domain coefficient analysis on the target area, and calculate the square sum of all frequency domain coefficients in the target area as the regional energy value;

[0029] S334: comparing the regional energy value with a preset energy threshold, and if the regional energy value is greater than the energy threshold, classifying the target region as a high-frequency region;

[0030] S335: If the regional energy value is less than or equal to the energy threshold, classify the target region as a low-frequency region;

[0031] S336: repeating steps S332 to S335 until all regions in the scene frequency domain image are classified as high-frequency regions or low-frequency regions;

[0032] S337: Acquire multiple frames of scene frequency domain images, and select a scene frequency domain image as a current frame image;

[0033] S338: Using the optical flow method, analyze the position of each pixel point in the high-frequency area of ​​the current frame image and the position of each pixel point in the previous frame image to determine the optical flow field corresponding to each pixel point;

[0034] S339: Calculate the mean of the optical flow field corresponding to each pixel point to determine the mean of the optical flow corresponding to the high-frequency area in the current frame image;

[0035] S3310: When the optical flow mean is less than a preset optical flow threshold, modify the high-frequency area in the current frame image and embed the preset blind watermark information into the scene frequency domain image;

[0036] S3311: Repeat steps S337 to S3310 until the preset blind watermark information is embedded into all scene frequency domain images.

[0037] Preferably, the S5 includes:

[0038] S51: performing second-level statistics on the pre-collected delivery data according to the material segment information, and determining real-time evaluation parameters related to the evaluation of the original video material and the number of times the original video material is used in the advertisement delivery, wherein the real-time evaluation parameters at least include: the number of real-time material plays, the real-time material click rate, and the real-time material loss rate;

[0039] S52: according to the number of uses, if the original video material is used once, determining a single material evaluation result according to the evaluation parameter and the original video material position;

[0040] S53: If the original video material is used multiple times, a weighted average calculation is performed on the single material evaluation results corresponding to each original video material according to the real-time material playback number to determine multiple material evaluation results.

[0041] Preferably, the S52 includes:

[0042] S521: Obtain the total duration of the target video collection, and determine the material position level according to the original video material position and total duration;

[0043] S522: determining standard parameters according to the material position, wherein the standard parameters at least include a standard material play count, a standard material click rate, and a standard material loss rate;

[0044] S523: Calculate the standard parameter and the evaluation parameter to determine the single material evaluation result.

[0045] In a second aspect, the present invention provides a device for evaluating the effect of placing an advertisement video material, the device comprising:

[0046] The original video material acquisition module is used to acquire at least one original video material uploaded by the target object in the advertisement delivery scenario;

[0047] A scene switching detection module, used to perform scene switching detection on the original video material, and divide the original video material into a plurality of scene video segments according to the scene switching detection result;

[0048] The blind watermark embedding module is used to embed the preset blind watermark information into each frame image in each scene video clip by using the image processing technology based on frequency domain transformation, and edit the scene video clips after the blind watermark information is embedded to obtain the target video highlights;

[0049] A blind watermark extraction module is used to extract blind watermark information from a target video collection, and determine material segment information based on the extracted blind watermark information, wherein the material segment information at least includes the name and position of the original video material used in the target video collection;

[0050] The material evaluation module is used to evaluate each material segment according to the pre-collected delivery data information and the material segment information when the target video collection is delivered for advertising, and to summarize all material evaluation results to obtain a delivery effect evaluation result.

[0051] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect in the above-mentioned embodiment.

[0052] In a fourth aspect, an embodiment of the present invention further provides a storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method of the first aspect in the above-mentioned embodiment is implemented.

[0053] In summary, the beneficial effects of the present invention are as follows:

[0054] The present invention provides a method, device, equipment and storage medium for evaluating the effect of advertising video material delivery. The method comprises: obtaining at least one original video material uploaded by a target object in an advertising delivery scenario; performing scene switching detection on the original video material, and dividing the original video material into a plurality of scene video clips; using an image processing technology based on frequency domain transformation to embed preset blind watermark information into each frame image in each scene video clip, and editing each scene video clip after the blind watermark information is embedded to obtain a target video collection; performing blind watermark information extraction on the target video collection, and determining material clip information based on the extracted blind watermark information, wherein the material clip information at least includes the name of the original video material used in the target video collection and the original video material position; when the target video collection is used for advertising delivery, each material clip is evaluated based on the pre-collected delivery data information and the material clip information, and all material evaluation results are summarized to obtain a delivery effect evaluation result. The present invention performs scene switching detection on the original video material, divides the original video material into multiple scene video clips, and uses image processing technology based on frequency domain transformation to embed blind watermark information into each frame image of each scene clip, so as to ensure that each material clip can be uniquely identified. In the edited target video collection, the blind watermark information is extracted to accurately identify and track the location and usage of each original video material. When advertising is delivered, the delivery effect of each original video material is accurately evaluated based on the pre-collected delivery data information and the extracted material clip information. This not only solves the problem of weak correlation between the finished advertisement and the original video material, but also provides accurate material performance data through refined evaluation, which helps advertisers optimize material usage and improve the efficiency and effectiveness of advertising delivery. This systematic and automated processing and analysis process reduces manual intervention and subjective bias, and improves the accuracy and real-time performance of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment of the present invention will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0056] Figure 1 This is a schematic diagram of the overall working process of the method for evaluating the effect of placing advertising video materials in Example 1 of the present invention;

[0057] Figure 2 It is a schematic diagram of a process of performing scene switching detection on the original video material in Embodiment 1 of the present invention;

[0058] Figure 3 This is a schematic diagram of a process for determining the color histogram threshold in Embodiment 1 of the present invention;

[0059] Figure 4 It is a schematic diagram of a process of embedding preset blind watermark information into each frame image in each scene video clip in Embodiment 1 of the present invention;

[0060] Figure 5 It is a schematic diagram of a process of extracting blind watermark information from a target video collection obtained by editing video clips of each scene after embedding blind watermark information in Embodiment 1 of the present invention;

[0061] Figure 6 This is a schematic diagram of a process for evaluating each material segment in Embodiment 1 of the present invention;

[0062] Figure 7 This is a schematic diagram of a process for determining a single material evaluation result in Embodiment 1 of the present invention;

[0063] Figure 8 This is a structural block diagram of an advertising video material delivery effect evaluation device in Embodiment 2 of the present invention;

[0064] Fig. 9 It is a schematic diagram of the structure of the electronic device in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described in conjunction with the drawings in the embodiment of the present invention. It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. In the absence of further restrictions, the elements defined by the phrase "comprising..." do not exclude the existence of other identical elements in the process, method, article or device comprising the elements. If there is no conflict, the embodiments of the present invention and the various features in the embodiments can be combined with each other, all within the protection scope of the present invention.

[0066] Example 1

[0067] See also Figure 1 Embodiment 1 of the present invention discloses a method for evaluating the effect of advertising video material delivery, the method comprising:

[0068] S1: Obtain at least one original video material uploaded by the target object in the advertising delivery scenario;

[0069] Specifically, obtaining the original video material uploaded by the target object in the advertising delivery scenario means first receiving the video file uploaded by the user and using it as the initial input data. The original video material contains various types of content, such as product advertisements, brand promotions, film and television drama clips, and user-generated content (UGC). The original video material covers different scenes, themes, and styles, and is diverse and complex. By further processing these original video materials, the foundation is laid for the subsequent material segment identification and evaluation, so that when the original video materials are edited and delivered, it is ensured that the usage and delivery effect of each material segment can be accurately extracted and analyzed.

[0070] S2: performing scene switching detection on the original video material, and dividing the original video material into a plurality of scene video segments according to the scene switching detection result;

[0071] Specifically, scene switching detection of the original video material refers to the use of image processing and computer vision technology to automatically identify the transition points between different scenes in the video, such as the moment of switching from one scene to another. Based on these scene switching detection results, the original video material is divided into multiple scene video clips, each of which represents a continuous visual and content unit. This process can not only accurately locate the starting and ending positions of each scene, but also provide a clear segmentation basis for subsequent blind watermark embedding and material management, thereby ensuring that each clip can be independently and accurately tracked and analyzed in subsequent editing, delivery and effect evaluation. In this way, by refining the processing of video materials, each scene clip can be managed and utilized more efficiently, improving the accuracy of overall advertising delivery and the accuracy of effect evaluation.

[0072] In one embodiment, see Figure 2 , said S2 comprises:

[0073] S21: Decomposing the original video material into multiple frames of original video images, and obtaining a color histogram corresponding to each original video image;

[0074] Specifically, decomposing the original video material into multiple frames of original video images is the first step in scene switching detection. First, the continuous original video material is decomposed into separate original video images. The video per second usually contains multiple frames (such as 30 frames per second). Each frame of the image represents an instantaneous picture in the video. Next, the color histogram of each frame of the original video image is calculated. The color histogram is a statistical chart that represents the number of pixels of each color in the image. By calculating the color histogram of each frame (denoted as H_i), the color distribution characteristics of each frame of the original video image can be obtained. These features are used in subsequent scene switching detection.

[0075] S22: performing difference calculation on the color histograms of the original video images of adjacent frames to obtain a color histogram difference value;

[0076] Specifically, after obtaining the color histogram of each frame of the original video image, the color histogram difference between adjacent frames is calculated, which is achieved by calculating the difference of the color histograms of adjacent frames. Commonly used methods include relative entropy (KL divergence) or L2 norm. For frames F_i and F_{i+1}, the color histogram difference D_i=d(H_i,H_{i+1}) is calculated, where d can be KL divergence or L2 norm. In this way, the degree of change in the color distribution of adjacent frame images is quantified. The calculation of color histogram difference is an important basis for detecting scene switching, because scene switching is usually accompanied by significant image color changes.

[0077] S23: If the color histogram difference value is greater than a preset color histogram threshold, the adjacent frames of original video images are classified into two scene video segments.

[0078] Specifically, in order to determine whether there is a scene switch, the calculated color histogram difference value D_i of each frame is compared with the preset color histogram threshold T_i. In order to improve the robustness of the detection, an adaptive threshold is used, which is dynamically adjusted according to the changes in the current frame. If the color histogram difference value D_i of a frame is greater than the adaptive threshold T_i, it is considered that there is a scene switch between frames F_i and F_{i+1}. At this time, the original video images of adjacent frames are divided into two different scene video clips, thereby realizing the scene cutting of the video material. This method ensures that each scene video clip has consistent visual features and is significantly different from other scenes.

[0079] In one embodiment, see Figure 3 , the preset color histogram threshold is determined by the following steps:

[0080] S2301: Acquire a current frame image from adjacent frames of original video images;

[0081] Specifically, during the scene switch detection process, the original video material will be processed frame by frame. First, the current frame image in the adjacent frame original video image is obtained, that is, the frame currently being processed. This frame image is the target of the current analysis. By calculating the color histogram difference between it and the previous frame image, it can be determined whether there is a scene switch.

[0082] S2302: Determine a color histogram set of all frame images before the current frame image according to the current frame image;

[0083] Specifically, after acquiring the current frame image, the color histogram set of all frame images before the current frame image is collected. This set contains the color histogram (H_1, H_2, ..., H_{i-1}) of each frame from the beginning of the video to the current frame. These histograms represent the color distribution of each frame image. By analyzing these data, we can understand the color changes of the video in the previous frames. This information is very important for calculating the change trend of the current frame and the subsequent threshold adjustment.

[0084] S2303: Determine the color histogram threshold according to the color histogram set in combination with a preset smoothing parameter and an initial threshold.

[0085] Specifically, in order to make the detection more robust, an adaptive threshold is used to determine whether there is a scene switch. The threshold (T_i) of the current frame will be dynamically adjusted based on the color histogram set and combined with the preset smoothing parameter (α) and the initial threshold (T). The specific calculation method is T_i=αT+(1-α)M_i, where Mi is the average difference of the previous n frames, expressed as Mi=(1 / n)Σ_{j=i-n+1}^{i}D_j. In this way, the threshold can be dynamically adjusted according to the changes in the current frame, making the scene switch detection more sensitive and accurate. If the color histogram difference D_i between the current frame and the previous frame is greater than this adaptive threshold T_i, the system determines that there is a scene switch between the two frames. The setting of this adaptive threshold ensures flexible adjustment when processing different video materials, improving the robustness and accuracy of detection.

[0086] In one embodiment, the S23 further includes:

[0087] S231: If the color histogram difference value is greater than a preset color histogram threshold, adjacent audio frames corresponding to the original video images of adjacent frames are obtained;

[0088] Specifically, when the color histogram difference value of adjacent video frames exceeds the preset color histogram threshold, it is preliminarily judged that there may be a scene switch. At this time, in addition to relying on image information, further verification is required in combination with audio information. Therefore, the audio frames corresponding to the two video frames are extracted synchronously. An audio frame refers to a piece of audio data aligned with the video frame on the time axis. By obtaining these audio frames and analyzing the changes in the audio signal, it is possible to confirm whether a scene switch has actually occurred. This process ensures the synchronous processing of video and audio information, laying the foundation for subsequent multimodal analysis.

[0089] S232: using a preset audio feature extraction algorithm to perform audio analysis on the adjacent audio frames to determine the amplitude of audio feature changes between adjacent audio frames;

[0090] Specifically, after obtaining the audio frame, the adjacent audio frames are analyzed and an audio feature extraction algorithm is used to detect changes in the audio. The audio feature extraction algorithm includes spectrum analysis, pitch change detection, audio energy analysis, frequency band energy change, and audio event detection technology. A preset audio feature extraction algorithm is used to identify significant changes in the audio, such as the switching of background music, the beginning or end of a conversation, or sudden changes in ambient sound. The amplitude of the audio feature change is calculated by comparing the features of adjacent audio frames. This amplitude of change can reflect the intensity of change in the audio content in a short period of time, thereby providing an auxiliary basis for judging scene switching. If the audio feature changes significantly, it means that in addition to the visual scene, the audio environment has also changed significantly, which further supports the judgment of scene switching.

[0091] S233: inputting adjacent original video frames and adjacent audio frames into a pre-trained multimodal large model, and outputting text content information related to the scene;

[0092] Specifically, the raw video images of adjacent frames are input into a pre-trained multimodal large model for processing. The multimodal large model can comprehensively process visual and audio information, and automatically generate text descriptions related to the scene or extract text information in the scene through deep learning technology. This step includes optical character recognition (OCR) to detect and extract text content in video frames, such as logos, subtitles, or written information in the scene. In addition, the multimodal large model can also analyze the combination of visual features and audio features of the scene to generate descriptive text information. This text information may include an explanation of the scene content, capture of semantic changes, or even transcription of detected dialogues or important sounds. By generating or extracting this text information, the scene content can be further understood at the semantic level, providing richer information for scene switching.

[0093] S234: performing weighted analysis on the audio feature change amplitude and the text content according to preset weights, and determining whether to segment adjacent frames of original video images into two scene video segments based on the weighted analysis result.

[0094] Specifically, after the audio and text information is extracted, the audio feature change amplitude and text content are weighted analyzed according to the preset weights. The purpose of weighted analysis is to integrate multimodal information and make more accurate scene switching judgments. The preset weights can be adjusted according to the characteristics of the video content and the analysis objectives. For example, for music videos, the weight of audio features may be higher, while for videos with rich dialogue scenes, the weight of text content may be higher. Through this weighted analysis, audio, text and image information are combined, and the change amplitude between them is comprehensively considered. If the weighted analysis results show obvious changes, it will be judged that there is a scene switch, and the adjacent video frames will be divided into two different scene video clips. In this way, not only based on visual features, but also full use of audio and text information is made to ensure that the detection of scene switching is more accurate and reliable.

[0095] S3: Using the image processing technology based on frequency domain transformation, the preset blind watermark information is embedded into each frame image in each scene video clip, and the video clips of each scene after the blind watermark information is embedded are edited to obtain the target video highlights;

[0096] In one embodiment, see Figure 4 , said S3 comprises:

[0097] S31: performing color space conversion on each frame image in each scene video clip to obtain a target color channel;

[0098] Specifically, each frame of each scene video clip is converted from RGB color space to YUV color space. YUV color space is divided into three channels: Y channel (brightness), U channel (chroma), and V channel (concentration). The converted YUV image can more effectively separate color information from brightness information, so that we can operate on U and V channels without affecting the overall visual quality. Through this conversion, the target color channels (U and V) of each frame are obtained, laying the foundation for subsequent blind watermark embedding.

[0099] S32: performing discrete cosine transform on the target color channel to convert each frame image in each scene video clip into a scene frequency domain image;

[0100] Specifically, after obtaining the target color channel, these channels are subjected to discrete cosine transform, namely DCT. DCT is a frequency domain transform method that can transform images from the spatial domain to the frequency domain. Through DCT, the image is represented as a set of frequency components, where the low-frequency components contain the main image information, while the high-frequency components contain less detail information. This transformation enables us to process in the frequency domain to hide the blind watermark information while minimizing the impact on the visual quality of the image. After DCT processing, the U and V channels of each frame of the image are converted into frequency domain images, showing the weights of each frequency component.

[0101] S33: modifying the high-frequency region in the scene frequency domain image according to the preset key, and embedding the preset blind watermark information into the scene frequency domain image.

[0102] Specifically, a preset key is used to determine the specific location of the blind watermark information embedded in the frequency domain image. The key is a set of parameters used for positioning, ensuring that the location of the blind watermark embedding is consistent and traceable for each frame. The high-frequency area in the scene frequency domain image is selected for modification, because these areas contain less visual information, and the overall visual effect of the image is minimal after modification. By embedding the blind watermark information (i.e., a unique ID) in the high-frequency area, it is ensured that each frame of the image contains identifiable hidden information. This process converts the frequency domain image back to the spatial domain image through an inverse transform (IDCT), and finally completes the embedding of the blind watermark information. In this way, each frame of the image carries invisible identification information, ensuring that the material can be accurately identified and tracked when it is referenced in the film.

[0103] In one embodiment, the S33 includes:

[0104] S331: Obtain the size of the template of the area to be embedded according to the preset blind watermark information;

[0105] Specifically, we first need to clarify the information content of the blind watermark, such as text, image or other forms of data, and calculate a suitable area template size based on the complexity and amount of information of the watermark. For example, if the watermark is a short text, only a template size of 8x8 or 16x16 is required, while if the watermark is an image or larger data, a larger area is required. The choice of template size affects the clarity and visibility of the watermark. If it is too large, it will have a significant impact on the image after embedding, and if it is too small, the information cannot be fully transmitted. Therefore, a reasonable template size can improve the concealment and transmission efficiency of the watermark.

[0106] S332: selecting any target area in the scene frequency domain image according to the size of the template of the area to be embedded, wherein the size of the target area is the same as the size of the template of the area to be embedded;

[0107] Specifically, a target area with the same size as the template is selected from the scene frequency domain image. The selection method includes random selection or based on image features. For example, if a frequency domain image of a static scene is selected, an 8x8 area is randomly selected from it. In practical applications, the selection of the target area takes into account the content characteristics of the image, such as selecting an area with a relatively flat background to ensure the concealment of the watermark. If the location of the target area happens to be in a part with less texture changes, the visual effect will be better after embedding the watermark.

[0108] S333: Perform frequency domain coefficient analysis on the target area, and calculate the square sum of all frequency domain coefficients in the target area as the regional energy value;

[0109] Specifically, the frequency domain coefficients of the selected target area are analyzed, and an energy value reflecting the strength of the area is obtained by calculating the square sum of all frequency domain coefficients in the area. For example, when analyzing an 8x8 DCT coefficient matrix, each frequency domain coefficient in the matrix is ​​squared and summed, and the result is the energy value of the area. This value can indicate the details and strength of the area, and is usually used to evaluate whether it is suitable for embedding watermarks. For example, if the energy value of a region is 50, it means that the region is relatively weak.

[0110] S334: comparing the regional energy value with a preset energy threshold, and if the regional energy value is greater than the energy threshold, classifying the target region as a high-frequency region;

[0111] Specifically, the calculated regional energy value is compared with the preset energy threshold. If the regional energy value is greater than the threshold, it is marked as a high-frequency area. For example, assuming the preset energy threshold is 40, if the energy value of a target area is 45, it means that the area has strong details and is suitable for carrying watermarks. On the contrary, if the energy value is lower than the threshold, it is not suitable for watermark embedding. Through such classification, the system can effectively identify which areas can effectively carry watermark information without damaging the overall quality of the image.

[0112] S335: If the regional energy value is less than or equal to the energy threshold, classify the target region as a low-frequency region;

[0113] Specifically, when the regional energy value is less than or equal to the preset energy threshold, the region is marked as a low-frequency region. The low-frequency region usually contains fewer detail changes and is flatter, which is suitable for embedding watermarks without causing obvious visual interference. For example, if the energy value is 35, it means that the region is relatively smooth and suitable for carrying watermarks. Through such classification, a wise choice can be made between high-frequency and low-frequency regions to ensure the visual concealment of the watermark.

[0114] S336: repeating steps S332 to S335 until all regions in the scene frequency domain image are classified as high-frequency regions or low-frequency regions;

[0115] Specifically, the frequency domain image of the entire scene is comprehensively analyzed, and all target regions are evaluated one by one by repeating steps S332 to S335. The purpose of this is to ensure that each region is classified so that the subsequent watermark embedding strategy can be based on comprehensive data. For example, assuming that there are multiple 8x8 regions in an image, each region will be analyzed one by one until all regions are marked as high frequency or low frequency. This process ensures that the image can be comprehensively evaluated and the appropriate region can be selected for watermark embedding to the greatest extent.

[0116] S337: Acquire multiple frames of scene frequency domain images, and select a scene frequency domain image as a current frame image;

[0117] Specifically, the current frame image is obtained from multiple scene frequency domain images. Multiple frame images provide dynamic background information for watermark embedding, which helps to improve the robustness of the watermark. For example, in a video sequence, there may be dozens of frames of images. Selecting one frame from these frames as the current processing object can better analyze the motion of the current frame and compare it with the previous frame, which is convenient for embedding dynamic watermarks.

[0118] S338: Using the optical flow method, analyze the position of each pixel point in the high-frequency area of ​​the current frame image and the position of each pixel point in the previous frame image to determine the optical flow field corresponding to each pixel point;

[0119] Specifically, the optical flow method is used to analyze the motion of the high-frequency area in the current frame. The optical flow method can effectively extract motion information by calculating the changes in pixel positions in adjacent frames. For example, if in a video, a pixel in the high-frequency area of ​​the current frame moves 5 pixels to the left, while the pixel at the corresponding position in the previous frame remains unchanged, this motion information will be recorded and the optical flow field will be constructed. This process provides a basis for subsequent watermark embedding, identifying which areas have less motion and are therefore more suitable for embedding watermarks.

[0120] S339: Calculate the mean of the optical flow field corresponding to each pixel point to determine the mean of the optical flow corresponding to the high-frequency area in the current frame image;

[0121] Specifically, the mean of all high-frequency area pixels in the optical flow field is calculated to determine the overall motion state of the area. For example, if the optical flow values ​​of multiple pixels in the high-frequency area are 2, 3, 4, and 5, respectively, then their mean is 3.5, which can reflect the degree of motion of the area. If the mean is small, it means that the area has relatively little motion and is suitable for watermark embedding.

[0122] S3310: When the optical flow mean is less than a preset optical flow threshold, modify the high-frequency area in the current frame image and embed the preset blind watermark information into the scene frequency domain image;

[0123] Specifically, if the calculated optical flow mean is lower than the preset optical flow threshold, the high-frequency area of ​​the current frame will be modified to embed the blind watermark information. For example, if the optical flow threshold is set to 4 and the mean is 3.5, the area will be judged suitable for watermark embedding. At this time, the watermark information will be added to the frequency domain coefficients of the area to ensure the concealment and effectiveness of the watermark.

[0124] S3311: Repeat steps S337 to S3310 until the preset blind watermark information is embedded into all scene frequency domain images.

[0125] Specifically, finally, steps S337 to S3310 are executed in a loop, and each scene frequency domain image is processed frame by frame until all images are embedded with watermarks. This continuous processing method can ensure that the watermark information is effectively transmitted in multiple frames of images and improve the robustness and concealment of the watermark. By performing dynamic analysis in each frame, it is ensured that the watermark information can maintain consistency and stability in the entire video sequence.

[0126] S4: extracting blind watermark information from the target video collection, and determining material segment information based on the extracted blind watermark information, wherein the material segment information at least includes the name and position of the original video material used in the target video collection;

[0127] Specifically, each frame of the target video collection is first processed, and the image is converted from the frequency domain back to the spatial domain through the inverse discrete cosine transform (IDCT), and the blind watermark information embedded in the high-frequency area is extracted. The specific position and content of the blind watermark information in the image are accurately located using the preset key. Through the extracted blind watermark information, the system can identify the unique ID in each frame of the image and determine which original material clips are used in the target video collection. This process allows the source and location of each material clip to be accurately tracked even if the material is edited, mixed, or reused in different scenes, ensuring that the references of the material in the finished film can be perfectly identified and accurate data reflow can be achieved.

[0128] In one embodiment, see Figure 5 , said S4 comprises:

[0129] S41: decomposing the target video collection into multiple frames of target video images, performing discrete cosine transform on each of the target video images, and determining a target frequency domain image;

[0130] Specifically, the target video collection is decomposed into separate target video images, each frame of the target video image represents a moment in the video, and discrete cosine transform (DCT) is applied to each frame of the target video image to convert the target video image from the spatial domain to the frequency domain to determine the target frequency domain image. Through DCT, the image is decomposed into a series of frequency components, each of which represents the change of different frequencies in the image. These determined target frequency domain images reveal the high-frequency and low-frequency information of the image, providing the basis for the subsequent blind watermark extraction process. Through this conversion, the system is able to process and analyze the hidden information in the image in the frequency domain.

[0131] S42: Determine a target position containing blind watermark information in a target frequency domain image according to the key;

[0132] Specifically, after acquiring the target frequency domain image, the preset key is used to determine the specific location of the blind watermark information in these frequency domain images. The key is a set of predefined parameters used to identify the frequency components and locations where the blind watermark information is embedded. By referring to the key, the area containing the blind watermark information in the frequency domain image can be accurately found. This step ensures the accuracy of blind watermark extraction, because only the key can correctly decode the embedded hidden information. The use of the key enables the systematic location of the unique ID information hidden in each frame of the image.

[0133] S43: extracting and restoring blind watermark information from the target frequency domain image according to the target position, and determining the material clip information.

[0134] Specifically, after determining the target positions of the blind watermark information, these positions are processed to extract the hidden blind watermark information. The extraction process includes reading the modification information in the high-frequency area from the frequency domain image, and restoring the frequency domain information to the spatial domain through the inverse discrete cosine transform (IDCT). The extracted blind watermark information contains an embedded unique ID, which corresponds one-to-one to the original material clips. Through these IDs, it is possible to identify and confirm which original material clips are used in the target video highlights, ensuring accurate tracking of the material and the accuracy of data reflow. In this way, detailed usage and performance data can be provided for each material clip, thereby optimizing advertising delivery and content management.

[0135] S5: When the target video collection is used for advertisement delivery, each material segment is evaluated based on the pre-collected delivery data information and the material segment information, and all material evaluation results are summarized to obtain a delivery effect evaluation result.

[0136] Specifically, when the target video collection is used for advertising delivery, the collected delivery data is associated and analyzed with the previously extracted material clip information. The delivery data includes key indicators such as playback volume, click-through rate, viewing time, and user interaction. By matching these delivery data with the unique ID of each material clip, the performance of each material clip in different advertising delivery scenarios can be accurately evaluated. During the evaluation process, various indicators are comprehensively considered, the effects of the material clips are quantitatively scored, and a detailed evaluation report is generated. Finally, the material evaluation results are output to provide advertisers with targeted optimization suggestions to help them improve the utilization efficiency of materials and advertising effects in future advertising creation and delivery.

[0137] In one embodiment, see Figure 6 , the S5 comprises:

[0138] S51: performing second-level statistics on the pre-collected delivery data according to the material segment information, and determining real-time evaluation parameters related to the evaluation of the original video material and the number of times the original video material is used in the advertisement delivery, wherein the real-time evaluation parameters at least include: the number of real-time material plays, the real-time material click rate, and the real-time material loss rate;

[0139] Specifically, based on the material segment information, which at least includes the name of the original video material and the position of the original video material used in the target video collection, the collected advertising delivery data is refined and statistically analyzed in seconds. Specifically, for each original video material segment, its specific usage period in the video is first determined. For example, assuming that the original video material segment is used between the 2nd and 5th seconds of the video, the delivery data within these seconds will be focused on. For the number of real-time material playbacks, the number of playbacks at the 2nd second when the material begins to be used will be used as the evaluation benchmark; next, the average loss rate per second of the original video material segment during its usage period is calculated. The real-time material loss rate refers to the proportion of viewers leaving the video in a certain second. The loss rates from the 2nd to the 5th seconds are calculated respectively, and then the average of these four values ​​is calculated to obtain the average loss rate per second of the material. Similarly, the click-through rate per second is also calculated. The real-time material click-through rate reflects the interaction between the audience and the advertisement during the video viewing process. The click-through rates from the 2nd to the 5th seconds are counted respectively, and their average value is calculated to obtain the average click-through rate per second of the material. Through such second-level statistics, key evaluation parameters related to material evaluation can be extracted, including the number of plays, average churn rate and average click-through rate. These parameters provide basic data for subsequent material evaluation, making the evaluation process more accurate and scientific, thereby helping advertisers understand the specific performance of each material in different time periods and delivery scenarios, and optimize advertising delivery strategies.

[0140] S52: according to the number of uses, if the original video material is used once, determining a single material evaluation result according to the evaluation parameter and the original video material position;

[0141] Specifically, according to the number of uses, when the original video material is used once, the previously collected evaluation parameters and the original video material position will be used to calculate its single evaluation result. This step ensures that a single use of each material is evaluated in detail for subsequent comprehensive evaluation.

[0142] In one embodiment, see Figure 7 , the S52 comprises:

[0143] S521: Obtain the total duration of the target video collection, and determine the material position level according to the original video material position and total duration;

[0144] Specifically, according to the material clip information, determine the specific usage position of the material in the target video collection, which includes the start time and end time of the material, as well as the specific time period in the video. This step helps to understand the position of the material in the video and provides a reference for subsequent evaluation. According to the position of the material clip in the video and the total length of the video, the material position is divided into different gears. For example, the video can be divided into five gears: beginning, front, middle, back and end. Assuming that the total length of the video is 35 seconds, each gear is approximately 7 seconds. The center position of the material assumes that the material usage position is 12 seconds to 15.3 seconds, and the average is 13.65. Calculate and round down the center position of the material / segment interval, and calculate and determine the specific gear to which it belongs according to the center position of the material clip.

[0145] S522: determining standard parameters according to the material position, wherein the standard parameters at least include a standard material play count, a standard material click rate, and a standard material loss rate;

[0146] Specifically, after determining the specific position of the material, the standard parameters are determined according to the material position, wherein the standard parameters at least include the standard material play count, the standard material click-through rate and the standard material loss rate; and each position corresponds to different standard parameters. For example, reference indicators for 5 different positions are defined respectively, R[0].ctr represents the standard click-through rate of the beginning material, and R[4].play represents the standard play count of the ending material. The standard parameters provide a benchmark for subsequent evaluation, making the evaluation process more accurate and targeted.

[0147] S523: Calculate the standard parameters and the evaluation parameters to determine the evaluation result of the single material. Specifically, finally, the evaluation parameters of the material are compared and analyzed with the standard parameters to calculate the evaluation result of the single use of the material. The evaluation result includes the scores of various indicators, comprehensively considering the performance of the playback volume, click-through rate and churn rate, and finally obtaining a comprehensive score. The calculation formula is:

[0148]

[0149] Where ε=0.000001, x.score represents the comprehensive score, x.play represents the number of original video material plays, x.ctr represents the average click rate of the original video material per second, xl r represents the average loss rate of the original video material per second, x.pos represents the original video material position, R[x.pos].play represents the standard number of plays at the original video material position, R[x.pos].ctr represents the standard click rate at the original video material position, R[x.pos].lr represents the standard loss rate at the original video material position, and the symbol Indicates that the value is truncated according to the lower limit a and the upper limit b. This score reflects the overall effect of the material in a single use and provides advertisers with a specific evaluation basis.

[0150] S53: If the original video material is used multiple times, a weighted average calculation is performed on the single material evaluation results corresponding to each original video material according to the real-time material playback number to determine multiple material evaluation results.

[0151] Specifically, when the material is used multiple times, the evaluation results of each use of the material are weighted averaged. The weighting is based on the logarithmic number of plays for each use, ensuring that the number of uses with a large number of plays has a higher weight in the comprehensive evaluation. The calculation formula is as follows:

[0152]

[0153] Among them, X[i].play represents the number of original video material plays corresponding to the i-th use of the original video material; X[i].score represents the single material evaluation result corresponding to the i-th use of the original video material; in this way, a more comprehensive evaluation result can be obtained, reflecting the overall performance of the material in multiple uses. This weighted average calculation method improves the accuracy of the evaluation and helps advertisers better understand the comprehensive effect of the material in different delivery scenarios.

[0154] After S5, the method further includes:

[0155] S61: Obtaining the advertisement type and delivery platform when delivering advertisements to the target video highlights;

[0156] Specifically, the target video collection is obtained for advertising delivery, and the delivery platform and advertising type for the advertisement are targeted. For example, for traditional media such as television, factors such as channel switching time and audience viewing habits are considered when evaluating materials, while for streaming platforms such as short video platforms, user behavior data is analyzed when evaluating materials, such as the time and number of bullet comments, interactive behaviors such as likes and reposts, and the platform's content recommendation algorithm. In terms of advertising types, Vlog-type advertisements usually focus on the natural implantation of content and the personal style of the creator, while evaluation-type advertisements emphasize the performance display of the product and the trust of the audience. In this step, these data will be collected and analyzed to provide a basis for the subsequent formulation of advertising delivery strategies, ensuring that the advertising content and delivery methods match the characteristics of the platform and advertising type, thereby improving the dissemination effect and user acceptance of the advertisement.

[0157] S62: performing a secondary correction on the material evaluation result according to the advertisement type and the delivery platform, and using the corrected evaluation result as the delivery effect analysis result.

[0158] Specifically, after obtaining the characteristics of the advertising type and the delivery platform, the preliminary material evaluation results are corrected for the second time. This process combines the original evaluation results with the platform's user behavior patterns and advertising interaction data through in-depth analysis of the characteristics of the advertising type and the delivery platform. For example, in Vlog ads, platform users prefer natural and personalized content, so the scores of hard ads will be lowered, while the scores of content native ads will be increased; in evaluation ads, more attention will be paid to the clarity and credibility of product information to ensure that the advertising effect on a specific platform is optimal. In addition, for streaming media platforms, the focus will be on user interaction behavior data, such as bullet screens and likes, while in traditional media, more attention will be paid to the advertising broadcast time period and the audience's viewing habits. Through this series of corrections, the final evaluation results will be more accurate, which can better guide subsequent advertising delivery and effect evaluation, and ensure that the advertising achieves the expected communication effect.

[0159] Example 2

[0160] See also Figure 8 Embodiment 2 of the present invention further provides a device for evaluating the effect of placing an advertisement video material, the device comprising:

[0161] The material upload module is used to obtain the uploaded original video material;

[0162] A material segmentation module, used for performing scene switching detection on the original video material, and segmenting the original video material into a plurality of scene video segments according to the scene switching detection result;

[0163] A blind watermark embedding module is used to embed the preset blind watermark information into each frame image in each scene video clip by using the image processing technology based on frequency domain transformation;

[0164] A blind watermark extraction module is used to extract blind watermark information from a target video collection obtained by editing video clips of each scene after embedding blind watermark information, and determine the material clip information in the target video collection based on the extracted blind watermark information;

[0165] The material evaluation module is used to evaluate each material segment according to the pre-collected delivery data information and the material segment information when the target video collection is used for advertising delivery, and output the material evaluation result.

[0166] Specifically, an advertising video material delivery effect evaluation device provided by an embodiment of the present invention is adopted, and the device includes: a material uploading module, which is used to obtain uploaded original video material; a material segmentation module, which is used to perform scene switching detection on the original video material, and divide the original video material into multiple scene video clips according to the scene switching detection result; a blind watermark embedding module, which is used to embed preset blind watermark information into each frame image in each scene video clip by using image processing technology based on frequency domain transformation; a blind watermark extraction module, which is used to extract blind watermark information from a target video collection obtained by editing each scene video clip after embedding the blind watermark information, and determine the material clip information in the target video collection according to the extracted blind watermark information; a material evaluation module, which is used to evaluate each material clip according to pre-collected delivery data information and the material clip information when the target video collection is used for advertising delivery, and output a material evaluation result. The device performs scene switching detection on the original video material, divides the original video material into multiple scene video clips, and uses image processing technology based on frequency domain transformation to embed blind watermark information into each frame image of each scene clip, so as to ensure that each material clip can be uniquely identified. In the edited target video collection, the blind watermark information is extracted to accurately identify and track the location and usage of each original video material. When advertising is delivered, the delivery effect of each original video material is accurately evaluated based on the pre-collected delivery data information and the extracted material clip information. This not only solves the problem of weak correlation between the finished advertisement and the original video material, but also provides accurate material performance data through refined evaluation, which helps advertisers optimize the use of materials and improve the efficiency and effectiveness of advertising delivery. This systematic and automated processing and analysis process reduces human intervention and subjective bias, and improves the accuracy and real-time performance of the evaluation.

[0167] Example 3

[0168] In addition, combined Figure 1The advertisement video material delivery effect evaluation method described in Embodiment 1 of the present invention may be implemented by an electronic device. Fig. 9 A schematic diagram of the hardware structure of an electronic device provided in Embodiment 3 of the present invention is shown.

[0169] The electronic device may include a processor and a memory storing computer program instructions.

[0170] Specifically, the processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present invention.

[0171] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory may be inside or outside a data processing device. In a specific embodiment, the memory is a non-volatile solid-state memory. In a specific embodiment, the memory includes a read-only memory (ROM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0172] The processor implements any one of the advertising video material delivery effect evaluation methods in the above embodiments by reading and executing computer program instructions stored in the memory.

[0173] In one example, the electronic device may further include a communication interface and a bus. Fig. 9 As shown, the processor, memory, and communication interface are connected via a bus and communicate with each other.

[0174] The communication interface is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiments of the present invention.

[0175] The bus includes hardware, software or both, and the parts of the device are coupled to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In suitable cases, the bus may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.

[0176] The electronic device provided in this embodiment performs scene switching detection on the original video material, divides the original video material into multiple scene video clips, and uses image processing technology based on frequency domain transformation to embed blind watermark information into each frame image of each scene clip, so as to ensure that each material clip can be uniquely identified. In the edited target video collection, the position and usage of each original video material can be accurately identified and tracked by extracting the blind watermark information. When advertising is delivered, the delivery effect of each original video material can be accurately evaluated based on the pre-collected delivery data information and the extracted material clip information. This not only solves the problem of weak correlation between the finished advertisement and the original video material, but also provides accurate material performance data through refined evaluation, which helps advertisers optimize material usage and improve the efficiency and effectiveness of advertising delivery. This systematic and automated processing and analysis process reduces manual intervention and subjective bias, and improves the accuracy and real-time performance of the evaluation.

[0177] Example 4

[0178] In addition, in combination with the advertising video material delivery effect evaluation method in the above embodiment 1, the present invention can also provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the advertising video material delivery effect evaluation methods in the above embodiments is implemented.

[0179] The storage medium provided in this embodiment performs scene switching detection on the original video material, divides the original video material into multiple scene video clips, and uses image processing technology based on frequency domain transformation to embed blind watermark information into each frame image of each scene clip, so as to ensure that each material clip can be uniquely identified. In the edited target video collection, the location and usage of each original video material can be accurately identified and tracked by extracting the blind watermark information. When advertising is delivered, the delivery effect of each original video material can be accurately evaluated based on the pre-collected delivery data information and the extracted material clip information. This not only solves the problem of weak correlation between the finished advertisement and the original video material, but also provides accurate material performance data through refined evaluation, which helps advertisers optimize material usage and improve the efficiency and effectiveness of advertising delivery. This systematic and automated processing and analysis process reduces manual intervention and subjective bias, and improves the accuracy and real-time performance of the evaluation.

[0180] In summary, the embodiments of the present invention provide a method, device, equipment and storage medium for evaluating the effect of advertising video material delivery.

[0181] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0182] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant location, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0183] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0184] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating the effect of advertising video material delivery, characterized in that: The method comprises: S1: Obtain at least one original video material uploaded by the target object in the advertising delivery scenario; S2: performing scene switching detection on the original video material, and dividing the original video material into a plurality of scene video segments according to the scene switching detection result; S3: Using the image processing technology based on frequency domain transformation, the preset blind watermark information is embedded into each frame image in each scene video clip, and the video clips of each scene after the blind watermark information is embedded are edited to obtain the target video highlights; S4: extracting blind watermark information from the target video collection, and determining material segment information based on the extracted blind watermark information, wherein the material segment information at least includes the name and position of the original video material used in the target video collection; S5: When the target video collection is used for advertisement delivery, each material segment is evaluated based on the pre-collected delivery data information and the material segment information, and all material evaluation results are summarized to obtain a delivery effect evaluation result.

2. The method for evaluating the effect of advertising video material delivery according to claim 1, characterized in that: The S2 includes: S21: Decomposing the original video material into multiple frames of original video images, and obtaining a color histogram corresponding to each original video image; S22: performing difference calculation on the color histograms of the original video images of adjacent frames to obtain a color histogram difference value; S23: If the color histogram difference value is greater than a preset color histogram threshold, the adjacent frames of original video images are classified into two scene video segments.

3. The method for evaluating the effect of placing advertising video materials according to claim 2, characterized in that: The preset color histogram threshold is determined by the following steps: S2301: Acquire a current frame image from adjacent frames of original video images; S2302: Determine a color histogram set of all frame images before the current frame image according to the current frame image; S2303: Determine the color histogram threshold according to the color histogram set in combination with a preset smoothing parameter and an initial threshold.

4. The method for evaluating the effect of advertising video material delivery according to claim 1, characterized in that: The S3 includes: S31: performing color space conversion on each frame image in each scene video clip to obtain a target color channel; S32: performing discrete cosine transform on the target color channel to convert each frame image in each scene video clip into a scene frequency domain image; S33: modifying the high-frequency region in the scene frequency domain image according to the preset key, and embedding the preset blind watermark information into the scene frequency domain image.

5. The method for evaluating the effect of advertising video material delivery according to claim 4, characterized in that: The S33 includes: S331: Obtain the size of the template of the area to be embedded according to the preset blind watermark information; S332: selecting any target area in the scene frequency domain image according to the size of the template of the area to be embedded, wherein the size of the target area is the same as the size of the template of the area to be embedded; S333: Perform frequency domain coefficient analysis on the target area, and calculate the square sum of all frequency domain coefficients in the target area as the regional energy value; S334: comparing the regional energy value with a preset energy threshold, and if the regional energy value is greater than the energy threshold, classifying the target region as a high-frequency region; S335: If the regional energy value is less than or equal to the energy threshold, classify the target region as a low-frequency region; S336: repeating steps S332 to S335 until all regions in the scene frequency domain image are classified as high-frequency regions or low-frequency regions; S337: Acquire multiple frames of scene frequency domain images, and select a scene frequency domain image as a current frame image; S338: Using the optical flow method, analyze the position of each pixel point in the high-frequency area of ​​the current frame image and the position of each pixel point in the previous frame image to determine the optical flow field corresponding to each pixel point; S339: Calculate the mean of the optical flow field corresponding to each pixel point to determine the mean of the optical flow corresponding to the high-frequency area in the current frame image; S3310: When the optical flow mean is less than a preset optical flow threshold, modify the high-frequency area in the current frame image and embed the preset blind watermark information into the scene frequency domain image; S3311: Repeat steps S337 to S3310 until the preset blind watermark information is embedded into all scene frequency domain images.

6. The method for evaluating the effect of placing advertising video materials according to any one of claims 1 to 5, characterized in that: The S5 includes: S51: performing second-level statistics on the pre-collected delivery data according to the material segment information, and determining real-time evaluation parameters related to the evaluation of the original video material and the number of times the original video material is used in the advertisement delivery, wherein the real-time evaluation parameters at least include: the number of real-time material plays, the real-time material click rate, and the real-time material loss rate; S52: according to the number of uses, if the original video material is used once, determining a single material evaluation result according to the evaluation parameter and the original video material position; S53: If the original video material is used multiple times, a weighted average calculation is performed on the single material evaluation results corresponding to each original video material according to the real-time material playback number to determine multiple material evaluation results.

7. The method for evaluating the effect of placing advertising video materials according to claim 6, characterized in that: The S52 includes: S521: Obtain the total duration of the target video collection, and determine the material position level according to the original video material position and total duration; S522: Determine standard parameters according to the material position and gear evaluation, wherein the standard parameters at least include the number of standard material plays, the standard material click rate, and the standard material loss rate; S523: Calculate the standard parameter and the evaluation parameter to determine the single material evaluation result.

8. An advertising video material delivery effect evaluation device, characterized in that: The device comprises: The original video material acquisition module is used to acquire at least one original video material uploaded by the target object in the advertisement delivery scenario; A scene switching detection module, used to perform scene switching detection on the original video material, and divide the original video material into a plurality of scene video segments according to the scene switching detection result; The blind watermark embedding module is used to embed the preset blind watermark information into each frame image in each scene video clip by using the image processing technology based on frequency domain transformation, and edit the scene video clips after the blind watermark information is embedded to obtain the target video highlights; A blind watermark extraction module is used to extract blind watermark information from a target video collection, and determine material segment information based on the extracted blind watermark information, wherein the material segment information at least includes the name and position of the original video material used in the target video collection; The material evaluation module is used to evaluate each material segment according to the pre-collected delivery data information and the material segment information when the target video collection is delivered for advertising, and to summarize all material evaluation results to obtain a delivery effect evaluation result.

9. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, implement the method according to any one of claims 1 to 7.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images