CN109933186B - Mobile web browser energy consumption optimization method based on screen brightness adjustment - Google Patents
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Abstract
A mobile Web browser energy consumption optimization method based on screen brightness adjustment comprises the steps of 1) utilizing an interface of an open source project chrome applied by a Webkit to collect data and implement an optimization scheme, compiling the interface to serve as a Web browser tested by an experiment, 2) quantifying impression screen brightness factors, 3) realizing a mobile terminal embedded background system interface, 4) modeling all the factors influencing adjustment factors, processing collected different user data, realizing an SVM classifier, performing cross training on the classifier by using training data stored in a database, finally embedding an optimization model into a mobile terminal, 5) designing a visual compensation scheme after screen brightness adjustment, processing display contents according to screen brightness adjustment factors b and data in a frame cache, and displaying the processed display contents on a mobile terminal screen, wherein the maximum optimization rate is up to 30% by comparing energy consumption in a default screen brightness adjustment mode with energy consumption using an optimization method.
Description
Technical Field
The invention belongs to the technical field of mobile computing, and particularly relates to a mobile web browser energy consumption optimization method based on screen brightness adjustment.
Background
As technology has been continuously developed, smart mobile devices have reached a level of popularity, and at the same time, various mobile terminal applications are urgently consuming power of mobile terminals. It has been found that over 52% of users use Webkit-based Web browsers on a daily basis, in addition to many Webki-based applications. Through experimental measurement, the power consumption of the screen brightness accounts for a very significant part of the using process of the mobile phone, and the prior art does not provide a good scheme for reducing the energy consumption of a mobile terminal by adjusting the screen brightness aiming at the part of applications based on a large user group.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a mobile web browser energy consumption optimization method based on screen brightness adjustment. The method comprises the steps of establishing a general model aiming at SVM classification methods for different users in consideration of influences of different webpage contents on user experience, and training the classification model by acquiring screen brightness adjusted by systems under different browsing contents according to light intensity, screen contents and corresponding screen brightness accepted by the users at different times of different users. When a user browses contents by using a Webkit-based application, the content complexity of the next frame of contents to be displayed on a screen in a frame cache after Webkit rendering and the current screen brightness determined by illumination intensity are analyzed in advance, the screen brightness is dynamically adjusted according to a trained embedded model, the contents in a GPUBuffer cache are displayed after brightness compensation is carried out, the visual requirements of the user during browsing are met, meanwhile, lower screen brightness energy consumption is guaranteed, and the energy consumption of the mobile Web application is reduced from the software and hardware level.
In order to achieve the purpose, the invention adopts the technical scheme that:
a mobile web browser energy consumption optimization method based on screen brightness adjustment comprises the following steps:
1) Reading frame cache data in a GPUBuffer according to an interface of an open source project chrome based on Webkit application of Google, modifying frame cache content according to a brightness adjusting factor, acquiring data, implementing an optimization scheme, and compiling the frame cache data to serve as a Web browser for the experimental test;
the open source item chrome supports hardware accelerated rendering based on a Webkit kernel, has the functions of a browser, reading frame cache content from a GPUBuffer and writing the frame cache content into the GPUBuffer, and reads the content in the GPUBuffer through openGL context constructed by the chrome aiming at GPU hardware accelerated rendering in the data acquisition process, wherein the content is stored in an RGB space of each pixel point of a screen. When the optimization mode is used, the value of the pixel point subjected to brightness compensation is written into GPUBuffer according to the RGB space;
2) The experiment verifies that the main factors influencing the user experience due to the change of the screen brightness comprise browsing content and environment illumination intensity, the environment illumination intensity directly influences the basic screen brightness adjusted by the system, and the factors are quantized;
the main factors influencing the screen adjustment are selected and quantized, the light intensity determines the basic screen brightness adjusted by the system, and the screen brightness is properly reduced on the basis, so that the adjusted screen brightness and the screen brightness adjusted by the system are in a positive correlation relationship, the screen brightness is represented by a screen brightness grade value in a mobile phone, the Android mobile phone is selected for experiment, the screen brightness grade value level in the Android Galaxy S4 is between 1 and 15, the screen brightness factor b is calculated according to the applied grade value, and b = level/15;
on one hand, the content complexity of the browsing content affects the user experience, so that the adjustment of the screen brightness is affected, and the more complex the content is, the higher the requirement of the user on the screen brightness is. The content complexity is described by the entropy of the gray value of the pixel point; on the other hand, the browsing content also influences the reduction capability after light compensation, and therefore the lower limit of the screen brightness adjustability. Through experimental statistics, after the screen content is subjected to light compensation, if the brightness value of pixel points exceeding 12% cannot be restored, the use experience of a user can be influenced, and therefore 12% is set as a threshold value for screen light compensation adjustment, and when the proportion of the pixel points exceeds the threshold value, a screen brightness adjustment factor obtained by the classifier is linearly adjusted upwards according to the exceeding proportion
The functions of realizing the embedded background system interface of the mobile terminal comprise: automatically collecting model training data and model prediction data during use, and adjusting screen brightness to implement an optimization scheme;
the background system interface in the step 3) has the functions of: model training data and model prediction data as used are automatically collected, and screen brightness is adjusted to implement the optimization scheme. When a user browses by using the Webkit-based application, a background system automatically acquires model training data, wherein the data comprises: different users, different moments, system screen brightness under different browsing contents, screen browsing contents and corresponding screen brightness adjustment factors acceptable to the users;
when the optimization model is applied to browsing, the background system collects user data every 5s during use, and the data comprises: the user, the current moment, the current system screen brightness factor and the browsing content currently stored in the GPU cache are used as characteristic values to be input into the optimization model;
modeling all factors influencing the adjustment factors, processing the acquired different user data, designing and realizing an SVM classifier according to the characteristics of a data sample, performing cross training on the classifier by using training data stored in a database, and finally embedding an optimization model into a mobile terminal;
and 4) taking the training data acquired in the step 3 as an input value of an SVM optimization model, calculating an average value of data of different users as training data for establishing a universal model, setting parameters of the SVM by using python, then performing cross training by using a data set according to a proportion of 5
Designing a visual compensation scheme after the screen brightness is adjusted, processing the display content according to the screen brightness adjustment factor b and the data in the frame buffer, and then displaying the processed display content on the screen of the mobile terminal;
the vision compensation scheme in the step 5). In order to compensate for the difference and meet the user experience, the original RGB image is converted into a YUV image, and then the luminance Y value is compensated according to a formula Yl = b × min (Y/b, 255) ≈ Y.
The beneficial effects of the invention are:
in the use process of the mobile terminal, the power consumption of a screen is a very significant part, and meanwhile, in the process of browsing a webpage by using a mobile browser, the screen brightness acceptance degrees of users for different browsing contents are different, but the screen brightness adjusting mechanism of the existing system only considers the light intensity, does not adjust the screen brightness in combination with user experience, and causes the waste of energy resources. Aiming at the problems, an optimization method is designed, visual requirements of a user during browsing are met, meanwhile, low screen brightness energy consumption is guaranteed, and energy consumption of mobile Web application is reduced from software and hardware levels. The accuracy of the classifier generated by training is as high as 98%.
Through tests, when a user uses 2 different mobile phones under different ambient illumination and browses different contents, the energy consumption in the default screen brightness adjusting mode is compared with the energy consumption of the optimization method, optimization has certain optimization capacity, and the maximum energy consumption optimization rate is as high as 30%.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, but the present invention is not limited to the following embodiments.
A mobile web browser energy consumption optimization method based on screen brightness adjustment comprises the following steps:
1) Reading frame cache data in a GPUBuffer according to the interface of an open source project Chromium based on Webkit application of Google, modifying frame cache contents according to a brightness adjusting factor, acquiring data and implementing an optimization scheme, and compiling the frame cache data to serve as a test Web browser of the experiment;
the open source item chrome supports hardware accelerated rendering based on a Webkit kernel, has the functions of a browser, reading frame cache content from a GPUBuffer and writing the frame cache content into the GPUBuffer, and reads the content in the GPUBuffer through openGL context constructed by the chrome aiming at GPU hardware accelerated rendering in the data acquisition process, wherein the content is stored in an RGB space of each pixel point of a screen. When the optimization mode is used, the value of the pixel point subjected to brightness compensation is written into GPUBuffer according to the RGB space;
2) Experiments verify that the main factors influencing the user experience due to the change of the screen brightness comprise browsing content and ambient light intensity, and the ambient light intensity directly influences the basic screen brightness adjusted by the system and quantifies the factors;
the main factors influencing the screen adjustment are selected and quantized, the light intensity determines the basic screen brightness adjusted by the system, and the screen brightness is properly reduced on the basis, so that the adjusted screen brightness and the screen brightness adjusted by the system are in a positive correlation relationship, and the screen brightness is represented by a screen brightness grade value in the mobile phone;
the complexity of browsing content affects the user experience, so that the adjustment of the screen brightness is affected, the more complex the content is, the higher the requirement of the user on the screen brightness is, and therefore, the content complexity is described by the entropy of the gray value of the pixel point; the browsing content also affects the reduction capability after the light compensation, so that the lower limit of the adjustable screen brightness is affected, and after the screen content is subjected to the light compensation, if the brightness value of more than 12% of the pixel points cannot be reduced, the use experience of a user is affected, so that 12% of the pixel points are set as the threshold value of the screen light compensation adjustment, and when the proportion of the pixel points exceeds the threshold value, the screen brightness adjustment factor obtained by the classifier is linearly adjusted upwards according to the exceeding proportion;
3) The method realizes the mobile terminal embedded background system interface, and the background system interface function comprises the following steps: automatically collecting model training data and model prediction data during use, and adjusting screen brightness to implement an optimization scheme; when a user browses by using the Webkit-based application, a background system automatically acquires model training data, wherein the model training data comprises: different users, different moments, system screen brightness under different browsing contents, screen browsing contents and corresponding screen brightness adjustment factors acceptable by the users; when the optimization model is applied to browse, the background system collects user data every 5s during use, and the collected data comprises the following data: the user, the current time, the current system screen brightness factor and the browsing content currently stored in the GPU cache are used as characteristic values to be input into the optimization model;
4) Modeling aiming at all factors influencing the adjusting factors, processing the acquired different user data, designing and realizing an SVM classifier according to the characteristics of a data sample, performing cross training on the classifier by using training data stored in a database, and finally embedding an optimization model into a mobile terminal; the method comprises the steps that collected training data serve as input values of an SVM optimization model, in order to establish a general model, average values of data of different users are calculated and then serve as training data, python is used for setting parameters of the SVM, then cross training is conducted by utilizing a data set according to the proportion of 5;
5) Designing a visual compensation scheme after the screen brightness is adjusted, processing the display content according to the screen brightness adjustment factor b and the data in the frame buffer, and then displaying the processed display content on the screen of the mobile terminal; in the visual compensation scheme, the brightness of the data in the frame buffer after the brightness adjustment of the optimization scheme is darker than the original system adjustment value, the original RGB image is converted into a YUV image, and then the brightness Y value is compensated according to a formula of Yl = b × min (Y/b, 255) approximately equal to Y.
Examples
1) Inviting 20 volunteers to browse a webpage by using a developed browser, acquiring user training data by calling a background interface, recording the screen brightness of a system and the current screen browsing content when the user browses different browsing contents at different moments, reducing the screen brightness on the basis, performing optical compensation correspondingly, and recording screen brightness adjustment factors acceptable by the user. This is used as a complete piece of training data. Data when more than 500 web pages were viewed for 20 users and an average was calculated for data of different users.
2) And cross training the SVM classifier according to the more than 5000 pieces of data obtained after processing, and embedding the SVM classifier into the mobile terminal.
3) In the using process, the background system collects user data from the browser and the system every 5s, inputs the model, obtains the model and outputs the model to the screen brightness adjusting factor b.
4) And b, performing brightness compensation on the collected content displayed by the current frame cache, calculating the proportion of pixel points exceeding the light compensation range, performing linear calculation on the exceeding part if the content exceeds a threshold value, adjusting a screen brightness adjusting factor b, and finally writing the content subjected to the light compensation into the frame cache and then gradually adjusting the brightness to the brightness value obtained by the calculation of b.
Claims (1)
1. A mobile web browser energy consumption optimization method based on screen brightness adjustment is characterized by comprising the following steps:
1) Reading frame cache data in a GPUBuffer according to an interface of an open source project chrome based on Webkit application of Google, modifying frame cache content according to a brightness adjusting factor, acquiring data, implementing an optimization scheme, and compiling the frame cache data to serve as a Web browser for the experimental test;
the open source item chrome supports hardware accelerated rendering based on a Webkit kernel, has the functions of a browser, reading frame cache content from a GPUBuffer and writing the frame cache content into the GPUBuffer, and reads the content in the GPUBuffer through openGL context constructed by the chrome aiming at GPU hardware accelerated rendering in the data acquisition process, wherein the content is stored in an RGB space of each pixel point of a screen. When the optimization mode is used, the value of the pixel point subjected to brightness compensation is written into a GPUBuffer according to the RGB space;
2) The experiment verifies that the main factors influencing the user experience due to the change of the screen brightness comprise browsing content and environment illumination intensity, the environment illumination intensity directly influences the basic screen brightness adjusted by the system, and the factors are quantized;
the main factors influencing the screen adjustment are selected and quantized, the light intensity determines the basic screen brightness adjusted by the system, and the screen brightness is properly reduced on the basis, so that the adjusted screen brightness and the screen brightness adjusted by the system are in a positive correlation relationship, and the screen brightness is represented by a screen brightness grade value in the mobile phone;
the complexity of browsing the content affects the user experience, so that the adjustment of the screen brightness is affected, and the more complex the content is, the higher the requirement of the user on the screen brightness is, so that the content complexity is described by the entropy of the gray value of the pixel point; the browsing content also affects the reduction capability after optical compensation, so that the lower limit of adjustable screen brightness is affected, after the screen content is subjected to optical compensation, if the brightness values of more than 12% of the pixels cannot be reduced, the use experience of a user is affected, so that 12% of the pixels are set as the threshold value of the screen optical compensation adjustment, and when the proportion of the pixels exceeds the threshold value, the screen brightness adjustment factor obtained by the classifier is linearly adjusted upwards according to the exceeding proportion;
3) The method realizes the mobile terminal embedded background system interface, and the background system interface function comprises the following steps: automatically collecting model training data and model prediction data during use, and adjusting screen brightness to implement an optimization scheme; when a user browses by using the Webkit-based application, a background system automatically acquires model training data, wherein the model training data comprises: different users, different moments, system screen brightness under different browsing contents, screen browsing contents and corresponding screen brightness adjustment factors acceptable by the users; when the optimization model is applied to browse, the background system collects user data every 5s during use, and the collected data comprises the following data: the user, the current moment, the current system screen brightness factor and the browsing content currently stored in the GPU cache are used as characteristic values to be input into the optimization model;
4) Modeling aiming at all factors influencing the adjusting factors, processing the acquired different user data, designing and realizing an SVM classifier according to the characteristics of a data sample, performing cross training on the classifier by using training data stored in a database, and finally embedding an optimization model into a mobile terminal; the method comprises the steps that collected training data serve as input values of an SVM optimization model, in order to establish a general model, average values of data of different users are calculated and then serve as training data, python is used for setting parameters of the SVM, then cross training is conducted by utilizing a data set according to the proportion of 5;
5) Designing a visual compensation scheme after the screen brightness is adjusted, processing the display content according to the screen brightness adjustment factor b and the data in the frame buffer, and then displaying the processed display content on the mobile terminal screen; in the visual compensation scheme, after the brightness of the data in the frame buffer is adjusted according to the optimization scheme, the data becomes darker in brightness relative to an original system adjustment value, the original RGB image is converted into a YUV image, and then the brightness Y value is compensated according to a formula of Yl = b × min (Y/b, 255) approximately matching Y;
and processing the display content according to the screen brightness adjusting factor b and the data in the frame buffer, displaying the processed display content on the mobile terminal screen, and comparing the energy consumption in the default screen brightness adjusting mode with the energy consumption of the optimization method, wherein the maximum optimization rate is up to 30%.
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