Advertising video processing method and device

Abstract

The present disclosure provides an advertising video processing method and device, including splitting the advertising video to be processed into multiple frames and numbering each frame; after the target user watches the advertising video to be processed, the target user selects the remembered frames and the frames that are liked or disliked according to the multiple frames displayed; based on the number of target users who watch the advertising video to be processed and the number of target users who remember each frame, a memory index is determined; the first number of target users who like each frame and the second number of target users who dislike each frame are obtained respectively; based on the first number and the second number, and preset emotional parameters, an emotional index is determined; based on the memory index and the emotional index, the visual frame weight score of each frame of the advertising video to be processed is determined, and the visual frame weight score is sent to the server. The method disclosed in the present disclosure can solve the limitations of pre-testing of advertisements, and the sample coverage is wider, and the difficulty of sample collection is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of video technology, and in particular to an advertising video processing method and device. Background Art

[0002] At present, the pre-testing research of video advertisements is mainly based on qualitative seminars, face-to-face interviews and eye tracker tests. However, these research methods have certain limitations and pain points. It is easy to produce some psychological factors that affect the subjects, resulting in the test results not being objective and true. In addition, the execution cost is very high, sample collection is difficult to scale, and the scope of sample collection is also very limited. It is difficult to adapt to the current fast-paced research methods and cannot meet the urgent needs of enterprises in a timely manner.

[0003] Therefore, how to propose a solution to the above-mentioned technical problems is a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] The embodiments of the present disclosure provide an advertisement video processing method and device, which can solve the problem of selecting the best advertisement from different video advertisements.

[0005] A first aspect of the embodiments of the present disclosure provides an advertising video processing method, comprising:

[0006] Splitting the advertisement video to be processed into a plurality of frames, and numbering each frame, wherein the frame is used to indicate each frame of the video to be processed;

[0007] After watching the advertisement video to be processed, the target user selects the remembered frames and the frames that he likes or dislikes according to the displayed multiple frames;

[0008] Determining a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame;

[0009] respectively obtaining a first number of target users who like each image frame and a second number of target users who dislike each image frame;

[0010] Determining an emotion index based on the first number of people, the second number of people, and preset emotion parameters;

[0011] According to the memory index and the emotion index, a visual frame weight score of each frame of the to-be-processed advertisement video is determined, and the visual frame weight score is sent to a server.

[0012] In an optional implementation, the method for determining the memory index based on the target number of users who watched the advertisement video to be processed and the target number of users who remembered each frame includes:

[0013] The memory index is determined as shown in the following formula:

[0014]

[0015] Among them, RI m represents the memory index of the mth frame, m represents the number of frames, X m represents the number of people who remember the mth frame, and N represents the target number of users who watch the video advertisement to be processed.

[0016] In an optional implementation, the method for determining the emotion index based on the first number of people, the second number of people, and preset emotion parameters includes:

[0017] The sentiment index is determined according to the following formula:

[0018] ERI m =PERI m +NERI m

[0019]

[0020]

[0021] Among them, ERI m Represents sentiment index, PERI m Represents positive sentiment parameters, NERI m represents the negative sentiment parameter, X m The number of people who like the mth frame, Y m represents the number of people who dislike the mth frame, N represents the number of target users who watch the video advertisement to be processed, and m represents the number of frames.

[0022] In an optional implementation, the method for determining the visual frame weight score of the to-be-processed advertisement video according to the memory index and the emotion index includes:

[0023] The visual frame weight score of the advertisement video to be processed is determined according to the method shown in the following formula:

[0024] P m =r 1 RI m +r 2 ERI m

[0025] Among them, Pm represents the frame weight score, r 1 Represents the memory weight parameter corresponding to the memory index, r 2 Represents the sentiment weight parameter corresponding to the sentiment index, RI mRepresents the memory index of the mth frame, ERI m represents the sentiment index, and m represents the number of frames.

[0026] In an optional implementation, after sending the visual frame weight score to the server, the method further includes:

[0027] The target user's remembered frames, favorite frames, disliked frames, and corresponding numbers of frames of each type, the number of target users, and target user information are sent to the server, wherein the target user information includes any one of the target user's age, gender, and location.

[0028] According to a second aspect of the embodiments of the present disclosure, there is provided an advertising video processing device, including:

[0029] A splitting unit, used for splitting the advertisement video to be processed into a plurality of frames and numbering each frame, wherein the frame is used for indicating each frame of the video to be processed;

[0030] A first acquisition unit is used for the target user to select a remembered frame and a liked or disliked frame according to the displayed multiple frames after watching the advertisement video to be processed;

[0031] A memory unit, used to determine a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame;

[0032] A second acquisition unit is used to respectively acquire a first number of target users who like each image frame and a second number of target users who do not like each image frame;

[0033] An emotion unit, configured to determine an emotion index based on the first number of people, the second number of people, and preset emotion parameters;

[0034] The first sending unit is used to determine the visual frame weight score of each frame of the to-be-processed advertisement video according to the memory index and the emotion index, and send the visual frame weight score to the server.

[0035] In an optional implementation, the memory unit is further used for:

[0036] The memory index is determined as shown in the following formula:

[0037]

[0038] Among them, RI m represents the memory index of the mth frame, m represents the number of frames, X mrepresents the number of people who remember the mth frame, and N represents the target number of users who watch the video advertisement to be processed.

[0039] In an optional implementation, the emotion unit is further used for:

[0040] The sentiment index is determined according to the following formula:

[0041] ERI m =PERI m +NERI m

[0042]

[0043]

[0044] Among them, ERI m Represents sentiment index, PERI m Represents positive sentiment parameters, NERI m represents the negative sentiment parameter, X m The number of people who like the mth frame, Y m represents the number of people who do not like the mth frame to be processed, N represents the number of target users who watch the video advertisement to be processed, and m represents the number of frames.

[0045] In an optional implementation, the sending unit is further configured to:

[0046] The visual frame weight score of the advertisement video to be processed is determined according to the method shown in the following formula:

[0047] P m =r 1 RI m +r 2 ERI m

[0048] Among them, P m represents the frame weight score, r 1 Represents the memory weight parameter corresponding to the memory index, r 2 Represents the sentiment weight parameter corresponding to the sentiment index, RI m Represents the memory index of the mth frame, ERI m represents the sentiment index, and m represents the number of frames.

[0049] In an optional implementation, the device further includes a second sending unit, wherein the second sending unit is configured to:

[0050] The target user's remembered frames, favorite frames, disliked frames, and corresponding numbers of frames of each type, the number of target users, and target user information are sent to the server, wherein the target user information includes any one of the target user's age, gender, and location.

[0051] The advertising video processing method provided by the present disclosure includes: splitting the advertising video to be processed into multiple frames, and numbering each frame, wherein the frame is used to indicate each frame of the video to be processed;

[0052] After watching the advertisement video to be processed, the target user selects the remembered frames and the frames that he likes or dislikes according to the displayed multiple frames;

[0053] Determining a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame;

[0054] The first number of target users who like each image frame and the second number of target users who dislike each image frame are obtained respectively:

[0055] Determining an emotion index based on the first number of people, the second number of people, and preset emotion parameters;

[0056] According to the memory index and the emotion index, a visual frame weight score of each frame of the to-be-processed advertisement video is determined, and the visual frame weight score is sent to a server.

[0057] The advertising video processing method disclosed in the present invention solves the limitations of previous advertising pre-tests from the project execution level, and can be applied to online network surveys, with a wider sample coverage and reduced difficulty in sample collection. From the resource cost level, it saves the time cost, manpower cost and capital investment of finding the target test population in market research for enterprises; online testing is conducive to viewers expressing their subjective attitudes more realistically and reducing human factors that affect viewers' feelings; it can obtain video advertising pre-test results more quickly and efficiently, and more intuitively reflect the attractiveness of the tested advertisements, thereby providing a basis for judging advertisements. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of the advertising video processing method according to an embodiment of the present disclosure;

[0059] Figure 2 It is a schematic diagram of the structure of the advertising video processing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0061] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in sequences other than those illustrated or described herein.

[0062] It should be understood that in the various embodiments of the present disclosure, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0063] It should be understood that in the present disclosure, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0064] It should be understood that in the present disclosure, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0065] It should be understood that in the present disclosure, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0066] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0067] The technical solution of the present disclosure is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0068] Figure 1 The flowchart of the advertising video processing method according to the embodiment of the present disclosure is shown as an example. Figure 1 As shown, the method includes:

[0069] Step S101: split the advertisement video to be processed into multiple frames, and number each frame;

[0070] Wherein, the image frame is used to indicate each frame of the video to be processed;

[0071] Exemplarily, each frame in a video advertisement can be defined as a frame, and the attractiveness of the frame to viewers is regarded as the frame weight. The larger the frame weight, the higher the ability to attract attention, and the greater the weight. The advertising clip can be split into n frames through video processing technology, and the frames can be numbered and marked respectively.

[0072] Step S102: After watching the advertisement video to be processed, the target user selects the remembered frames and the frames that he likes or dislikes according to the displayed multiple frames;

[0073] After watching the advertisement, the target user can select the impressive frames and the frames with emotional fluctuations from the n displayed frames, and record the numbers corresponding to the corresponding frames.

[0074] Step S103, determining a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame;

[0075] In an optional implementation, the method for determining the memory index based on the target number of users who watched the advertisement video to be processed and the target number of users who remembered each frame includes:

[0076] The memory index is determined as shown in the following formula:

[0077]

[0078] Among them, RI m represents the memory index of the mth frame, m represents the number of frames, X mrepresents the number of people who remember the mth frame, and N represents the target number of users who watch the video advertisement to be processed.

[0079] Step S104, respectively obtaining a first number of target users who like each image frame and a second number of target users who dislike each image frame;

[0080] Step S105: determining an emotion index based on the first number of people, the second number of people, and preset emotion parameters;

[0081] In an optional implementation, the method for determining the emotion index based on the first number of people, the second number of people, and preset emotion parameters includes:

[0082] The sentiment index is determined according to the following formula:

[0083] ERI m =PERI m +NERI m

[0084]

[0085]

[0086] Among them, ERI m Represents sentiment index, PERI m Represents positive sentiment parameters, NERI m represents the negative sentiment parameter, X m The number of people who like the mth frame, Y m represents the number of people who dislike the mth frame, N represents the number of target users who watch the video advertisement to be processed, m Indicates the number of image frames.

[0087] Step S106: determining a visual frame weight score of each frame of the to-be-processed advertisement video according to the memory index and the emotion index, and sending the visual frame weight score to a server.

[0088] In an optional implementation, the method for determining the visual frame weight score of the to-be-processed advertisement video according to the memory index and the emotion index includes:

[0089] The visual frame weight score of the advertisement video to be processed is determined according to the method shown in the following formula:

[0090] P m =r 1 RI m +r 2 ERI m

[0091] Among them, P m represents the frame weight score, r 1 Represents the memory weight parameter corresponding to the memory index, r 2 Represents the sentiment weight parameter corresponding to the sentiment index, RI m Represents the memory index of the mth frame, ERI m represents the sentiment index, and m represents the number of frames.

[0092] In an optional implementation, after sending the visual frame weight score to the server, the method further includes:

[0093] The target user's remembered frames, favorite frames, disliked frames, and corresponding numbers of frames of each type, the number of target users, and target user information are sent to the server, wherein the target user information includes any one of the target user's age, gender, and location.

[0094] The advertising video processing method provided by the present disclosure includes: splitting the advertising video to be processed into multiple frames, and numbering each frame, wherein the frame is used to indicate each frame of the video to be processed;

[0095] After watching the advertisement video to be processed, the target user selects the remembered frames and the frames that he likes or dislikes according to the displayed multiple frames;

[0096] Determining a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame;

[0097] respectively obtaining a first number of target users who like each image frame and a second number of target users who dislike each image frame;

[0098] Determining an emotion index based on the first number of people, the second number of people, and preset emotion parameters;

[0099] According to the memory index and the emotion index, a visual frame weight score of each frame of the to-be-processed advertisement video is determined, and the visual frame weight score is sent to a server.

[0100] The advertising video processing method disclosed in the present invention solves the limitations of previous advertising pre-tests from the project execution level, and can be applied to online network surveys, with a wider sample coverage and reduced difficulty in sample collection. From the resource cost level, it saves the time cost, manpower cost and capital investment of finding the target test population in market research for enterprises; online testing is conducive to viewers expressing their subjective attitudes more realistically and reducing human factors that affect viewers' feelings; it can obtain video advertising pre-test results more quickly and efficiently, and more intuitively reflect the attractiveness of the tested advertisements, thereby providing a basis for judging advertisements.

[0101] Figure 2 The structural diagram of the advertising video processing device according to the embodiment of the present disclosure is shown as an example. Figure 2 As shown, the device comprises:

[0102] The splitting unit 21 is used to split the advertisement video to be processed into a plurality of frames and number each frame, wherein the frame is used to indicate each frame of the video to be processed;

[0103] A first acquisition unit 22 is used for the target user to select a remembered frame and a liked or disliked frame according to the displayed multiple frames after watching the advertisement video to be processed;

[0104] A memory unit 23, used to determine a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame;

[0105] A second acquisition unit 24 is used to respectively acquire a first number of target users who like each image frame and a second number of target users who do not like each image frame;

[0106] An emotion unit 25, configured to determine an emotion index based on the first number of people, the second number of people, and preset emotion parameters;

[0107] The first sending unit 26 is used to determine the visual frame weight score of each frame of the to-be-processed advertisement video according to the memory index and the emotion index, and send the visual frame weight score to the server.

[0108] In an optional implementation, the memory unit 23 is further used for:

[0109] The memory index is determined as shown in the following formula:

[0110]

[0111] Among them, RI m represents the memory index of the mth frame, m represents the number of frames, X mrepresents the number of people who remember the mth frame, and N represents the target number of users who watch the video advertisement to be processed.

[0112] In an optional implementation, the emotion unit 25 is further used for:

[0113] The sentiment index is determined according to the following formula:

[0114] ERI m =PERI m +NERI m

[0115]

[0116]

[0117] Among them, ERI m Represents sentiment index, PERI m Represents positive sentiment parameters, NERI m represents the negative sentiment parameter, X m The number of people who like the mth frame, Y m represents the number of people who dislike the mth frame, N represents the number of target users who watch the video advertisement to be processed, and m represents the number of interested frames.

[0118] In an optional implementation manner, the first sending unit 26 is further configured to:

[0119] The visual frame weight score of the advertisement video to be processed is determined according to the method shown in the following formula:

[0120] P m =r 1 RI m +r 2 ERI m

[0121] Among them, P m represents the frame weight score, r 1 Represents the memory weight parameter corresponding to the memory index, r 2 Represents the sentiment weight parameter corresponding to the sentiment index, RI m Represents the memory index of the mth frame, ERI m represents the sentiment index, and m represents the number of frames.

[0122] In an optional implementation, the device further includes a second sending unit, and the second sending unit is configured to:

[0123] The target user's remembered frames, favorite frames, disliked frames, and corresponding numbers of frames of each type, the number of target users, and target user information are sent to the server, wherein the target user information includes any one of the target user's age, gender, and location.

[0124] The present disclosure also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the various embodiments described above.

[0125] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, abbreviated as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0126] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present disclosure may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for processing an advertising video, It is characterized in that include: Splitting the advertisement video to be processed into a plurality of frames, and numbering each frame, wherein the frame is used to indicate each frame of the advertisement video to be processed; After watching the advertisement video to be processed, the target user selects the remembered frames and the frames that he likes or dislikes according to the displayed multiple frames; Determining a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame; respectively obtaining a first number of target users who like each image frame and a second number of target users who dislike each image frame; Determining an emotion index based on the first number of people, the second number of people, and preset emotion parameters; Determine the visual frame weight score of each frame of the to-be-processed advertisement video according to the memory index and the emotion index, and send the visual frame weight score to the server; The method for determining the memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame includes: The memory index is determined as shown in the following formula: Among them, RI m represents the memory index of the mth frame, m represents the number of frames, X m represents the number of people who remember the mth frame, and N represents the number of target users who watch the advertisement video to be processed; The method for determining the emotion index based on the first number of people, the second number of people, and preset emotion parameters includes: The sentiment index is determined according to the following formula: DIFFERENT m =INHERIT m +NERI m Among them, ERI m Represents sentiment index, PERI m Represents positive sentiment parameters, NERI m represents the negative sentiment parameter, X m The number of people who like the mth frame, Y m represents the number of people who dislike the mth frame, N represents the number of target users who watch the advertisement video to be processed, and m represents the number of frames; The method for determining the visual frame weight score of the advertisement video to be processed according to the memory index and the emotion index comprises: The visual frame weight score of the advertisement video to be processed is determined according to the method shown in the following formula: P m = r 1 RI m +r 2 ERI m Among them, P m represents the frame weight score, r 1 Represents the memory weight parameter corresponding to the memory index, r 2 Represents the sentiment weight parameter corresponding to the sentiment index, RI m Represents the memory index of the mth frame, ERI m represents the sentiment index, and m represents the number of frames.

2. The advertising video processing method according to claim 1, It is characterized in that After sending the visual frame weight score to the server, the method further includes: The target user's remembered frames, favorite frames, disliked frames, and corresponding numbers of frames of each type, the number of target users, and target user information are sent to the server, wherein the target user information includes any one of the target user's age, gender, and location.

3. An advertising video processing device, It is characterized in that include: A splitting unit, used for splitting the advertisement video to be processed into a plurality of frames and numbering each frame, wherein the frame is used for indicating each frame of the advertisement video to be processed; A first acquisition unit is used for the target user to select a remembered frame and a liked or disliked frame according to the displayed multiple frames after watching the advertisement video to be processed; A memory unit, used to determine a memory index based on the number of target users who watched the advertisement video to be processed and the number of target users who remembered each frame; A second acquisition unit is used to respectively acquire a first number of target users who like each image frame and a second number of target users who do not like each image frame; An emotion unit, configured to determine an emotion index based on the first number of people, the second number of people, and preset emotion parameters; A first sending unit, configured to determine a visual frame weight score of each frame of the to-be-processed advertisement video according to the memory index and the emotion index, and send the visual frame weight score to a server; The memory unit is also used for: The memory index is determined as shown in the following formula: Among them, RI m represents the memory index of the mth frame, m represents the number of frames, X m represents the number of people who remember the mth frame, and N represents the number of target users who watch the advertisement video to be processed; The emotion unit is also used to: The sentiment index is determined according to the following formula: DIFFERENT m =INHERIT m +NERI m Among them, ERI m Represents sentiment index, PERI m Represents positive sentiment parameters, NERI m represents the negative sentiment parameter, X m The number of people who like the mth frame, Y m represents the number of people who dislike the mth frame, N represents the number of target users who watch the advertisement video to be processed, and m represents the number of frames; The first sending unit is further used for: The visual frame weight score of the advertisement video to be processed is determined according to the method shown in the following formula: P m = r 1 RI m +r 2 ERI m Among them, P m represents the frame weight score, r 1 Represents the memory weight parameter corresponding to the memory index, r 2 Represents the sentiment weight parameter corresponding to the sentiment index, RI m Represents the memory index of the mth frame, ERI m represents the sentiment index, and m represents the number of frames.

4. The device according to claim 3, It is characterized in that The device further includes a second sending unit, wherein the second sending unit is configured to: The target user's remembered frames, favorite frames, disliked frames, and corresponding numbers of frames of each type, the number of target users, and target user information are sent to the server, wherein the target user information includes any one of the target user's age, gender, and location.

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