CN114025104B - Color image display device and color image display system - Google Patents
Color image display device and color image display system Download PDFInfo
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Abstract
The invention belongs to the field of image display, relates to a color image display technology, and is used for solving the problem that the existing image display device and image display system cannot classify and manage images through color parameters of the images, in particular to a color image display device and a color image display system, wherein an image processor is in communication connection with an image quality analysis module, a storage module, a calling analysis module, an image classification module, an image receiving module and a display module; the image quality analysis module is used for detecting and analyzing the quality of the color image through the image parameters and obtaining an image quality coefficient; according to the invention, the calling habit of the user can be analyzed according to the calling coefficients of the first-level image, the second-level image and the third-level image, the value analysis is carried out on the images of different levels, and the value analysis result is matched with the aesthetic habit of the user, so that the low-value images in different levels are processed according to the aesthetic habit of the user.
Description
Technical Field
The invention belongs to the field of image display, relates to a color image display technology, and in particular relates to a color image display device and a color image display system.
Background
By connecting the information processing apparatus and the image display apparatus via the image transmission cable so as to transmit an image signal from the information processing apparatus to the image display apparatus, thereby causing the image display apparatus to display an image that can be displayed on the information processing apparatus such as a personal computer; further, as the network environment is widely spread, image data generated in the information processing apparatus can be transmitted to the image display apparatus via the network, and the image display apparatus can enlarge and display the received image data.
The conventional image display device and image display system do not have the function of classifying and managing images through color parameters of the images, and further cannot evaluate the value of the images according to the types through calling parameters of the images, so that a large amount of low-value images exist in one type to occupy a storage space.
Disclosure of Invention
The invention aims to provide a color image display device and a color image display system, which are used for solving the problem that the prior image display device and image display system cannot classify and manage images through color parameters of the images;
the technical problems to be solved by the invention are as follows: how to provide an image display system that can make a classification evaluation of the value of color images.
The aim of the invention can be achieved by the following technical scheme:
the color image display system comprises an image processor, wherein the image processor is in communication connection with an image analysis module, a storage module, a call analysis module, an image classification module, an image receiving module and a display module;
the color image is sent to an image processor through an image receiving module, and the image processor sends the received color image to a display module for display;
the image quality analysis module is used for detecting and analyzing the quality of the color image through the image parameters and obtaining an image quality coefficient;
the image quality analysis module sends the analysis image and the image quality coefficient of the analysis image to the image classification module through the image processor, the image classification module receives the analysis image and then carries out salient level judgment on the analysis image, and the analysis image is judged to be a primary image, a secondary image or a tertiary image;
the calling analysis module is used for carrying out calling condition analysis on the analysis images stored in the storage module and deleting the low-value images through the value analysis model.
As a preferred embodiment of the invention, the specific process of detecting and analyzing the quality of the color image by the image analysis module comprises the following steps:
and marking the color image to be detected as an analysis image, acquiring the resolution of the analysis image and marking the resolution as FB, acquiring the memory value of the analysis image and marking the memory value as NC, and obtaining the image quality coefficient TZ of the analysis image through a formula TZ=α1×FB+α2×NC, wherein α1 and α2 are both proportional coefficients, and α1 > α2 > 1.
As a preferred embodiment of the invention, the specific process of the image classification module for performing the prominence level judgment on the analysis image comprises the following steps:
amplifying the analysis image into a pixel grid image, and carrying out gray level transformation on the pixel grid image to obtain a contrast image; marking pixel cells of a contrast image as i, i=1, 2, …, n and n as positive integers, marking the gray value of the pixel cell i as HDi, comparing the gray value HDi of the pixel cell i with a gray threshold value HDmax one by one, marking the pixel cell with the gray value HDi not smaller than the gray threshold value as salient pixels, obtaining the number of the salient pixels and marking the number as m;
performing contact judgment on the salient pixels and outputting a numerical value o of the salient region;
obtaining a contrast value DD of the contrast image through a formula DD= (m/n+o/m) x TZ, comparing the contrast value DD with contrast threshold values DDmin and DDmax, and judging the salient level of the analysis image through a comparison result;
the image classification module sends the analysis images and the salient levels of the analysis images to the storage module for storage through the image processor.
As a preferred embodiment of the present invention, the process of contact determination includes: setting the initial value of the protruding area to be zero, selecting one protruding pixel as a calibration pixel, and if the protruding pixels do not exist in the four pixel grids contacted with the calibration pixel, adding the number of the protruding areas and selecting the next protruding pixel as the calibration pixel; if the protruding pixels exist in the four pixel grids contacted with the calibration pixels, marking the protruding pixels in the four pixel grids as the calibration pixels, judging whether the protruding pixels exist in the four pixel grids contacted with the calibration pixels again until the protruding pixels do not exist in the four pixel grids contacted with the calibration pixels, and adding the number of the protruding areas together to select the next protruding pixel as the calibration pixel; the above operation is repeated until all the protruding pixels are selected as the calibration pixels.
As a preferred embodiment of the present invention, the comparison process of the comparison value DD and the comparison threshold values DDmin, DDmax includes:
if DD is less than or equal to DDmin, judging that the salient level of the contrast image is three-level, and the corresponding contrast image is a three-level image;
if DDmin is less than DD and less than DDmax, judging the salient level of the contrast image as a second level, and the corresponding contrast image as a second level image;
if DD is more than or equal to DDmax, judging the salient level of the contrast image as a first-level image, and judging the corresponding contrast image as a first-level image.
As a preferred embodiment of the present invention, the specific process of calling the analysis module to analyze the call condition of the analysis image stored in the storage module includes: acquiring the total number of analysis images called by a storage module in L1 day and marking the total number as ZS, respectively marking the numbers of primary images, secondary images and tertiary images as Q1, Q2 and Q3, wherein L1 is a time constant, respectively marking the numbers of repeated calls in the primary images, the secondary images and the tertiary images as t1, t2 and t3, respectively obtaining call coefficients DY1, DY2 and DY3 of the primary images, the secondary images and the tertiary images through formulas DY 1-t 1)/ZS, DY 2= (Q2-t 2)/ZS and DY 3= (Q3-t 3)/ZS, and sending the call coefficients of the primary images, the secondary images and the tertiary images to a display module for display through an image processor;
and comparing the calling coefficients of the primary image, the secondary image and the tertiary image in numerical value, and marking the image grade with the highest calling coefficient value as a high-quality grade.
As a preferred embodiment of the invention, the specific process of the value analysis model for performing value analysis on the primary image comprises the following steps: the first-level image is marked as an image u, u=1, 2, …, w and w are positive integers, the total number of times the image u is called in L2 days is obtained and marked as CSu, the interval time between the first call and the second call of the image u is marked as JG1, and the interval time JG1 is compared with an interval threshold value JGmin: if JG1 is less than or equal to JGmin, judging that the second call of the image is invalid and repeated, and subtracting one from the value of the total call times CSu; if JG is larger than JGmin, judging that the second call of the image is effectively repeated, wherein the total number CSu of calls is unchanged; the interval time between the second call and the third call of the image u is marked as JG2, the interval time JG2 is compared with an interval threshold value JGmin, and the like until the comparison of the interval time of the last call of the image u and the interval threshold value is completed;
acquiring the total duration of the image u displayed by the display module within L2 days, marking the total duration as SCu, obtaining a value coefficient JZu of the image u through a formula JZu =β1× CSu +β2×SCu, comparing the value coefficient JZu of the image u with value thresholds JZmin and JZmax, and judging whether the image u is a low-value image or not through a comparison result;
and deleting the low-value image in the storage module.
As a preferred embodiment of the present invention, the comparison process of the value coefficient JZu of the image u with the value thresholds JZmin, JZmax includes:
if JZu is less than or equal to JZmin, judging the image u as a low-value image;
if JZ is less than JZu and less than JZmax, judging the image u as a qualified image;
if JZu is more than or equal to JZmax, the image u is judged to be a high-value image.
The color image display device comprises a display panel, wherein a main screen, a first auxiliary screen, a second auxiliary screen and an electric quantity display are arranged on the front surface of the display panel, the main screen is used for displaying a color image, and the first auxiliary screen is used for displaying the image quality coefficient, the contrast value and the value coefficient of a current display image; the second sub-screen is used for displaying the number of the primary images, the secondary images and the tertiary images stored in the storage module.
The invention has the following beneficial effects:
1. the image quality analysis module is used for detecting and analyzing the whole quality of the color image according to the image parameters to obtain image quality coefficients, the whole display value of the color image is reflected by the image quality coefficients, and meanwhile, the numerical value of the image quality coefficients also has weight influence on the acquisition of contrast values, so that the salient level of the contrast image can be influenced through the image parameters, and the salient level judgment result is more accurate;
2. the image classification module amplifies an analysis image into a pixel grid image, then carries out gray level conversion on the pixel grid image to obtain a gray level image, then judges whether the pixel grid is a salient pixel according to a comparison result of a gray level value and a gray level threshold value of each pixel grid, meanwhile carries out contact judgment on the salient pixel, reflects the distribution condition of the salient pixel in the pixel grid image through a contact judgment result, and if the number of the salient regions is larger, the distribution of the salient pixel in the pixel grid image is more divergent, otherwise, the distribution of the salient pixel in the pixel grid image is more concentrated, so that a comparison value is obtained through the numerical calculation of the salient pixel and the salient region, and judges the salient level of the comparison image according to the comparison result of the comparison value and the comparison threshold value, the color image is classified according to the integral condition of the color salient degree and the color distribution of the color image, and the aesthetic of a user can be judged through calling data;
3. and carrying out calling condition analysis on the primary image, the secondary image and the tertiary image through a calling analysis module and obtaining calling coefficients, so that calling habits of users are analyzed according to the calling coefficients of the primary image, the secondary image and the tertiary image, value analysis is carried out on images of different grades, value analysis results are matched with aesthetic habits of the users, and low-value images in different grades are processed according to the aesthetic habits of the users.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of a first embodiment of the present invention;
fig. 2 is a front view of a structure of a second embodiment of the present invention.
In the figure: 1. a display panel; 2. a main screen; 3. a first secondary screen; 4. a second secondary screen; 5. an electrical quantity display.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a color image display system includes an image processor, which is communicatively connected with an image analysis module, a storage module, a call analysis module, an image classification module, an image receiving module and a display module;
the color image is sent to an image processor through an image receiving module, and the image processor sends the received color image to a display module for display;
the image analysis module is used for detecting and analyzing the quality of the color image through image parameters, wherein the image parameters comprise the resolution and the memory value of the color image, and the image resolution refers to the number of pixels (in units of pixels/inch or pixels/cm) contained in the unit length of the image along the width direction and the height direction. For the same image, the higher the resolution, the finer the description of the image, and the larger the amount of data required; the lower the resolution, the coarser the image, the smaller the data volume, and the specific detection analysis process comprises:
the method comprises the steps of marking a color image to be detected as an analysis image, obtaining the resolution of the analysis image and marking the resolution as FB, obtaining the memory value of the analysis image and marking the memory value as NC, and obtaining the image quality coefficient TZ of the analysis image through a formula TZ=α1×FB+α2×NC, wherein α1 and α2 are both proportional coefficients, and α1 > α2 > 1, and it is to be noted that the image quality coefficient TZ is a numerical value reflecting the display value of the analysis image, and the larger the numerical value of the image quality coefficient TZ is, the higher the display value of the analysis image is.
The image quality analysis module sends the analysis image and the image quality coefficient of the analysis image to the image classification module through the image processor, the image classification module receives the analysis image and then carries out the salient level judgment on the analysis image, and the specific process of carrying out the salient level judgment on the analysis image comprises the following steps:
the method comprises the steps of amplifying an analysis image into a pixel grid image, carrying out gray level transformation on the pixel grid image to obtain a contrast image, wherein the gray level transformation refers to a method for changing the gray level value of each pixel in a source image point by point according to a certain target condition and a certain transformation relation, so that the display effect of the image is clearer for improving the image quality, and the gray level transformation processing of the image is a very basic and direct spatial domain image processing method in an image enhancement processing technology and is also an important component of image digitizing software and image display software; marking pixel cells of a contrast image as i, i=1, 2, …, n and n as positive integers, marking the gray value of the pixel cell i as HDi, comparing the gray value HDi of the pixel cell i with a gray threshold value HDmax one by one, marking the pixel cell with the gray value HDi not smaller than the gray threshold value HDmax as salient pixels, obtaining the number of the salient pixels and marking the number as m;
the process of performing a touch determination on the protruding pixels and outputting the value o of the protruding area includes: setting the initial value of the protruding area to be zero, selecting one protruding pixel as a calibration pixel, and if the protruding pixels do not exist in the four pixel grids contacted with the calibration pixel, adding the number of the protruding areas and selecting the next protruding pixel as the calibration pixel; if the protruding pixels exist in the four pixel grids contacted with the calibration pixels, marking the protruding pixels in the four pixel grids as the calibration pixels, judging whether the protruding pixels exist in the four pixel grids contacted with the calibration pixels again until the protruding pixels do not exist in the four pixel grids contacted with the calibration pixels, and adding the number of the protruding areas together to select the next protruding pixel as the calibration pixel; repeating the above operation until all the salient pixels are selected as the calibration pixels, integrating the salient pixels which are in contact with each other into a salient region through contact judgment, so as to feed back the distribution condition of the salient pixels in the pixel grid image according to the quantity of the salient regions, wherein the more the quantity of the salient regions is, the more divergent the distribution of the salient pixels in the pixel grid image is, and otherwise, the more concentrated the distribution of the salient pixels in the pixel grid image is;
the contrast value DD of the contrast image is obtained by the formula dd= (m/n+o/m) ×tz, and it is to be noted that the contrast value DD is a value fed back from the angle of color highlighting and distribution to the image characteristics, a larger value of the contrast value DD indicates that the color of the contrast image is more highlighted and dispersed, the color performance is stronger, and the contrast value DD is compared with the contrast threshold values DDmin and DDmax:
if DD is less than or equal to DDmin, judging that the salient level of the contrast image is three-level, and the corresponding contrast image is a three-level image;
if DDmin is less than DD and less than DDmax, judging the salient level of the contrast image as a second level, and the corresponding contrast image as a second level image;
if DD is more than or equal to DDmax, judging the salient level of the contrast image as a first level, and the corresponding contrast image as a first level image;
the image classification module sends the analysis images and the salient levels of the analysis images to the storage module for storage through the image processor.
The call analysis module is used for analyzing call conditions of the analysis images stored in the storage module, acquiring the total number of the analysis images called by the storage module within L1 days, marking the numbers of the primary images, the secondary images and the tertiary images as Q1, Q2 and Q3 respectively, wherein L1 is a time constant, marking the times of repeated calling in the primary images, the secondary images and the tertiary images as t1, t2 and t3 respectively, respectively obtaining call coefficients DY1, DY2 and DY 3= (Q1-t 1)/ZS, DY 2= (Q2-t 2)/ZS and DY 3= (Q3-t 3)/ZS of the primary images, the secondary images and the tertiary images respectively, and sending the call coefficients of the primary images, the secondary images and the tertiary images to the display module through the image processor to display, wherein the call coefficients are values of a response image call frequency, and the higher call coefficients represent the higher call frequency of the images;
comparing the calling coefficients of the primary image, the secondary image and the tertiary image in numerical value, and marking the image grade with the highest calling coefficient value as a high-quality grade;
sending the analysis image and the salient level of the analysis image to a value analysis model for value analysis, wherein the specific process of the value analysis model for value analysis of the primary image comprises the following steps: the first-level image is marked as an image u, u=1, 2, …, w and w are positive integers, the total number of times the image u is called in L2 days is obtained and marked as CSu, the interval time between the first call and the second call of the image u is marked as JG1, and the interval time JG1 is compared with an interval threshold value JGmin: if JG1 is less than or equal to JGmin, judging that the second call of the image is invalid and repeated, and subtracting one from the value of the total call times CSu; if JG is larger than JGmin, judging that the second call of the image is effectively repeated, wherein the total number CSu of calls is unchanged; the interval time between the second call and the third call of the image u is marked as JG2, the interval time JG2 is compared with an interval threshold value JGmin, and the like until the comparison of the interval time of the last call of the image u and the interval threshold value is completed, so that the call habits of users are analyzed according to the call coefficients of the primary image, the secondary image and the tertiary image, value analysis is carried out on the images of different grades, the value analysis result is matched with the aesthetic habits of the users, and the low-value images in different grades are processed according to the aesthetic habits of the users;
the total duration of the display of the image u by the display module within L2 days is acquired and marked as SCu, the value coefficient JZu of the image u is obtained through the formula JZu =β1× CSu +β2×scu, and the value coefficient JZu of the image u is compared with the value thresholds JZmin and JZmax:
if JZu is less than or equal to JZmin, judging the image u as a low-value image;
if JZ is less than JZu and less than JZmax, judging the image u as a qualified image;
if JZu is more than or equal to JZmax, judging the image u as a high-value image;
and deleting the low-value image in the storage module.
The value analysis model performs value analysis on the secondary image and the tertiary image in the same manner.
Example two
Referring to fig. 2, a color image display device includes a display panel 1, wherein a main screen 2, a first auxiliary screen 3, a second auxiliary screen 4 and an electric quantity display 5 are disposed on the front surface of the display panel 1, the main screen 2 is used for displaying a color image, and the first auxiliary screen 3 is used for displaying a quality coefficient, a contrast value and a value coefficient of a current display image; the second sub-screen 4 is used for displaying the number of the primary images, the secondary images and the tertiary images stored in the storage module.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula tz=α1×fb+α2×nc; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding image coefficient for each group of sample data; substituting the set image coefficient and the acquired sample data into a formula, forming a binary one-time equation set by any two formulas, screening the calculated coefficient and taking an average value to obtain values of alpha 1 and alpha 2 of 2.87 and 2.54 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding image coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the image coefficient is proportional to the value at the resolution.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (1)
1. The color image display system comprises an image processor, and is characterized in that the image processor is in communication connection with an image analysis module, a storage module, a calling analysis module, an image classification module, an image receiving module and a display module;
the color image is sent to an image processor through an image receiving module, and the image processor sends the received color image to a display module for display;
the image quality analysis module is used for detecting and analyzing the quality of the color image through the image parameters and obtaining an image quality coefficient;
the image quality analysis module sends the analysis image and the image quality coefficient of the analysis image to the image classification module through the image processor, the image classification module receives the analysis image and then carries out salient level judgment on the analysis image, and the analysis image is judged to be a primary image, a secondary image or a tertiary image;
the calling analysis module is used for carrying out calling condition analysis on the analysis images stored in the storage module and deleting the low-value images through the value analysis model;
the specific process of the image quality analysis module for detecting and analyzing the color image comprises the following steps:
marking a color image to be detected as an analysis image, acquiring the resolution of the analysis image and marking the resolution as FB, acquiring the memory value of the analysis image and marking the memory value as NC, and obtaining the image quality coefficient TZ of the analysis image through a formula TZ=α1×FB+α2×NC, wherein α1 and α2 are both proportional coefficients, and α1 > α2 > 1;
the specific process of the image classification module for judging the saliency level of the analysis image comprises the following steps:
amplifying the analysis image into a pixel grid image, and carrying out gray level transformation on the pixel grid image to obtain a contrast image; marking pixel cells of a contrast image as i, i=1, 2, …, n and n as positive integers, marking the gray value of the pixel cell i as HDi, comparing the gray value HDi of the pixel cell i with a gray threshold value HDmax one by one, marking the pixel cell with the gray value HDi not smaller than the gray threshold value as salient pixels, obtaining the number of the salient pixels and marking the number as m;
performing contact judgment on the salient pixels and outputting a numerical value o of the salient region;
obtaining a contrast value DD of the contrast image through a formula DD= (m/n+o/m) x TZ, comparing the contrast value DD with contrast threshold values DDmin and DDmax, and judging the salient level of the analysis image through a comparison result;
the image classification module sends the analysis images and the salient levels of the analysis images to the storage module for storage through the image processor;
the contact determination process includes: setting the initial value of the protruding area to be zero, selecting one protruding pixel as a calibration pixel, and if the protruding pixels do not exist in the four pixel grids contacted with the calibration pixel, adding the number of the protruding areas and selecting the next protruding pixel as the calibration pixel; if the protruding pixels exist in the four pixel grids contacted with the calibration pixels, marking the protruding pixels in the four pixel grids as the calibration pixels, judging whether the protruding pixels exist in the four pixel grids contacted with the calibration pixels again until the protruding pixels do not exist in the four pixel grids contacted with the calibration pixels, and adding the number of the protruding areas together to select the next protruding pixel as the calibration pixel; repeating the above operation until all the protruding pixels are selected as the calibration pixels;
the comparison process of the comparison value DD and the comparison threshold DDmin and DDmax comprises the following steps:
if DD is less than or equal to DDmin, judging that the salient level of the contrast image is three-level, and the corresponding contrast image is a three-level image;
if DDmin is less than DD and less than DDmax, judging the salient level of the contrast image as a second level, and the corresponding contrast image as a second level image;
if DD is more than or equal to DDmax, judging the salient level of the contrast image as a first level, and the corresponding contrast image as a first level image;
the specific process of calling the analysis image stored in the storage module by the calling analysis module for analyzing the condition comprises the following steps: acquiring the total number of analysis images called by a storage module in L1 day and marking the total number as ZS, respectively marking the numbers of primary images, secondary images and tertiary images as Q1, Q2 and Q3, wherein L1 is a time constant, respectively marking the numbers of repeated calls in the primary images, the secondary images and the tertiary images as t1, t2 and t3, respectively obtaining call coefficients DY1, DY2 and DY3 of the primary images, the secondary images and the tertiary images through formulas DY 1-t 1)/ZS, DY 2= (Q2-t 2)/ZS and DY 3= (Q3-t 3)/ZS, and sending the call coefficients of the primary images, the secondary images and the tertiary images to a display module for display through an image processor;
comparing the calling coefficients of the primary image, the secondary image and the tertiary image in numerical value, and marking the image grade with the highest calling coefficient value as a high-quality grade;
the specific process of the value analysis model for carrying out value analysis on the primary image comprises the following steps: the first-level image is marked as an image u, u=1, 2, …, w and w are positive integers, the total number of times the image u is called in L2 days is obtained and marked as CSu, the interval time between the first call and the second call of the image u is marked as JG1, and the interval time JG1 is compared with an interval threshold value JGmin: if JG1 is less than or equal to JGmin, judging that the second call of the image is invalid and repeated, and subtracting one from the value of the total call times CSu; if JG is larger than JGmin, judging that the second call of the image is effectively repeated, wherein the total number CSu of calls is unchanged; the interval time between the second call and the third call of the image u is marked as JG2, the interval time JG2 is compared with an interval threshold value JGmin, and the like until the comparison of the interval time of the last call of the image u and the interval threshold value is completed;
acquiring the total duration of the image u displayed by the display module within L2 days, marking the total duration as SCu, obtaining a value coefficient JZu of the image u through a formula JZu =β1× CSu +β2×SCu, comparing the value coefficient JZu of the image u with value thresholds JZmin and JZmax, and judging whether the image u is a low-value image or not through a comparison result;
deleting the low-value image in the storage module;
the comparison process of the value coefficient JZu of the image u and the value threshold JZmin, JZmax includes:
if JZu is less than or equal to JZmin, judging the image u as a low-value image;
if JZ is less than JZu and less than JZmax, judging the image u as a qualified image;
if JZu is more than or equal to JZmax, the image u is judged to be a high-value image.
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