CN114339234B - Mobile equipment video stream transmission method based on brightness scaling - Google Patents
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Abstract
The embodiment of the disclosure provides a mobile device video streaming transmission method based on brightness scaling, which belongs to the technical field of electricity and specifically comprises the following steps: establishing a first association model; establishing a second association model; combining the first association model and the second association model according to a preset weight factor to obtain a video quality model; measuring the power consumption condition of the mobile equipment for transmitting and playing the video stream under different screen brightness to obtain a power consumption model; fitting a video quality model and a power consumption model by multiple linear regression to obtain an optimization target; according to the optimization objective, an optimal decision of the bit rate and the luminance scaling factor of the mobile device is calculated. By the scheme, scores of testers are collected, the existing brightness scaling mechanism is directly related to user perception, power consumption factors and the viewing quality of a video stream are formed into a deterministic optimization problem, an optimal decision is generated, and adaptability and energy conservation of video stream transmission are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of electricity, in particular to a mobile device video streaming method based on brightness scaling.
Background
The current development of the MPEG-DASH protocol makes adaptive video streaming over HTTP the best way to stream media transport. In the process of transmitting video streams by handheld devices, the rendering display of video and the downloading transmission of video blocks are the two most significant parts of energy consumption. Reducing screen brightness and selecting low bit rate video blocks is the most efficient means to save power, however, this approach can greatly reduce the user's actual viewing experience quality. Therefore, taking effective measures to save the power of the mobile device while maintaining a good video stream viewing experience is a challenge to be addressed. Many studies have focused on how to utilize a brightness scaling mechanism to reduce device power consumption, while researchers employ a network condition aware bit rate adaptation mechanism to save energy consumption of traffic transmissions. However, existing studies ignore a significant and critical factor: in a mobile scenario, such as sitting on a high-speed rail, public transportation, etc., the handheld device inevitably vibrates with environmental changes. Neglecting this factor, existing video streaming methods inevitably produce poor viewing experience and huge power consumption. At the same time, highly dynamic network environment conditions can also have a severe impact on the transmission of video streams, which can make the actual viewing experience of the user difficult to trade off.
As can be seen, there is a need for a mobile device video streaming method based on brightness scaling that improves the adaptability and energy-saving of video streaming.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method for video streaming of a mobile device based on brightness scaling, which at least partially solves the problems of poor adaptability and energy saving of video streaming in the prior art.
The embodiment of the disclosure provides a mobile device video streaming method based on brightness scaling, comprising the following steps:
respectively obtaining video quality scores of different testers under vibration grades corresponding to different scenes, and establishing a first association model according to the video quality scores;
Respectively obtaining video quality scores of different testers under brightness scaling factors corresponding to different scenes, and establishing a second association model according to the video quality scores;
Combining the first association model and the second association model according to a preset weight factor to obtain a video quality model;
Measuring the power consumption condition of the mobile equipment for transmitting and playing the video stream under different screen brightness, and establishing a relation function between the brightness and the power consumption according to the power consumption condition to obtain a power consumption model;
Multiple linear regression fits the video quality model and the power consumption model to obtain an optimization target;
and calculating the optimal decision of the bit rate and the brightness scaling factor of the mobile device according to the optimization target.
According to a specific implementation manner of the embodiment of the present disclosure, the step of respectively obtaining video quality scores of different testers under vibration levels corresponding to different scenes, and establishing a first association model according to the video quality scores includes:
obtaining video quality scores correspondingly input when the testers watch videos with different bit rates in preset scenes, wherein the preset scenes comprise static, sitting, walking or public transportation;
And fitting a mathematical model of the vibration grade and the video quality score by using a linear regression minimization MSE according to the video quality score, and obtaining the first association model.
According to a specific implementation manner of the embodiment of the present disclosure, the step of respectively obtaining video quality scores of different testers under brightness scaling factors corresponding to different scenes, and establishing a second association model according to the video quality scores includes:
obtaining video quality scores correspondingly input when the testers watch videos coded by different brightness scaling factors in preset scenes, wherein the preset scenes comprise static, sitting, walking or public transportation;
And fitting the brightness scaling factor and the mathematical model of the video quality score by using a linear regression minimization MSE according to the video quality score, and obtaining the second association model.
According to a specific implementation manner of the embodiment of the present disclosure, the step of measuring the power consumption of the mobile device for transmitting and playing the video stream under different screen brightness, and establishing a relation function between brightness and power consumption according to the power consumption, to obtain a power consumption model includes:
respectively measuring the power consumption of the mobile equipment for transmitting and playing video streams under different screen brightness until reaching a preset duration;
and according to the power consumption, obtaining the power consumption function model through linear regression minimization MSE fitting.
According to a specific implementation manner of the embodiment of the present disclosure, the expression of the optimization objective isWhere E (n) represents the power consumption of the nth video block, E max is the maximum value of the measured power consumption, Q (n) represents the video quality score of the nth video block, Q max is the maximum value of the measured video block quality, and γ is a parameter controlling the weight of both.
According to a specific implementation of an embodiment of the present disclosure, before the step of calculating the optimal decision of the bitrate and brightness scaling factor of the mobile device according to the optimization objective, the method further includes:
Sampling the flow in a preset time range, calculating a sliding window average value, obtaining estimated throughput and adding the estimated throughput to the optimization target.
According to a specific implementation manner of the embodiment of the present disclosure, the method further includes, before the step of obtaining the first correlation model, fitting a mathematical model of the vibration level and the video quality score with a linear regression minimized MSE according to the video quality score, wherein the method further includes:
And acquiring acceleration data in a preset sampling period, and obtaining the vibration grade according to the acceleration data.
According to a specific implementation of an embodiment of the disclosure, the step of calculating an optimal decision of the bitrate and brightness scaling factor of the mobile device according to the optimization objective includes:
And optimizing the optimization target by utilizing an optimizer to obtain the optimal decision of the bit rate and the brightness scaling factor of the mobile equipment.
A mobile device video streaming scheme based on brightness scaling in an embodiment of the present disclosure includes: respectively obtaining video quality scores of different testers under vibration grades corresponding to different scenes, and establishing a first association model according to the video quality scores; respectively obtaining video quality scores of different testers under brightness scaling factors corresponding to different scenes, and establishing a second association model according to the video quality scores; combining the first association model and the second association model according to a preset weight factor to obtain a video quality model; measuring the power consumption condition of the mobile equipment for transmitting and playing the video stream under different screen brightness, and establishing a relation function between the brightness and the power consumption according to the power consumption condition to obtain a power consumption model; multiple linear regression fits the video quality model and the power consumption model to obtain an optimization target; and calculating the optimal decision of the bit rate and the brightness scaling factor of the mobile device according to the optimization target.
The beneficial effects of the embodiment of the disclosure are that: by the scheme, scores of testers are collected, the existing brightness scaling mechanism is directly related to user perception, power consumption factors and the viewing quality of a video stream are formed into a deterministic optimization problem, an optimal decision is generated, and adaptability and energy conservation of video stream transmission are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a flowchart of a mobile device video streaming method based on brightness scaling according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram illustrating an effect of a different vibration scene on video quality viewed by a user according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating the effect of different brightness scaling factors on the video quality of the user viewing according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a relationship between different screen brightness and power consumption of a mobile device according to an embodiment of the disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides an adaptive and energy-saving method for video streaming, which can be applied to a video streaming optimization process of an internet data transmission scene.
Referring to fig. 1, a flow chart of an adaptive and energy-saving method for video streaming according to an embodiment of the disclosure is provided. As shown in fig. 1, the method mainly comprises the following steps:
S101, video quality scores of different testers under vibration levels corresponding to different scenes are respectively obtained, and a first association model is built according to the video quality scores;
optionally, in step S101, video quality scores of different testers under vibration levels corresponding to different scenes are obtained respectively, and a first correlation model is built according to the video quality scores, including:
obtaining video quality scores correspondingly input when the testers watch videos with different bit rates in preset scenes, wherein the preset scenes comprise static, sitting, walking or public transportation;
And fitting a mathematical model of the vibration grade and the video quality score by using a linear regression minimization MSE according to the video quality score, and obtaining the first association model.
Further, before the step of fitting the vibration level and the mathematical model of the video quality score to the video quality score using a linear regression minimization MSE to obtain the first correlation model, the method further includes:
And acquiring acceleration data in a preset sampling period, and obtaining the vibration grade according to the acceleration data.
In specific implementation, a plurality of mobile devices provided with vibration monitoring software can be held by a tester in a crowdsourcing mode, coded test videos are watched by using customized video players under static, sitting, walking and public transportation scenes, simultaneously video quality scores corresponding to the input of the tester when watching videos with different bit rates in preset scenes are recorded, and then a mathematical model of vibration grades and video quality scores is fitted by using a linear regression minimized MSE according to the video quality scores, so that the first correlation model is obtained. Before the method, vibration monitoring software can be adopted to obtain accelerations a m,x,am,y,am,z in three directions in the video stream transmission process by using an embedded acceleration sensor of the mobile phone, and in order to eliminate the influence of the gravity acceleration of the earth, the accelerations are processed, so that the gravity acceleration of the earth is removed. From these accelerations, the average motion amount m a within the sampling window can be calculated according to the following equation,
Where M is the size of the sampling window.
Further, the average change rate f a in the sampling window M is calculated as follows
Further, the vibration level v in the sampling time range can be calculated by the following formula
v＝β*ma+(1-β)*fa
Where β is an adjustable parameter that controls the weight of two variables, and in general, β can be set to 0.5 by considering both factors equally important.
Further, a loss amount I of vibration and bit rate to quality is calculated v
Iv＝c3+c4*exp(c5*b*v)
Wherein c 3,c4,c5 is tuning parameters, based on actual user evaluation, tuning is performed by means of linear fitting, b is the bit rate of the current video block, and v is the calculated vibration level. The impact of vibration of the mobile scene on the user's perception of the video stream is thus quantified according to the embedded acceleration sensor of the handheld device as shown in fig. 2.
S102, video quality scores of different testers under brightness scaling factors corresponding to different scenes are respectively obtained, and a second association model is built according to the video quality scores;
optionally, the step of respectively obtaining video quality scores of different testers under the brightness scaling factors corresponding to different scenes and establishing the second association model according to the video quality scores includes:
obtaining video quality scores correspondingly input when the testers watch videos coded by different brightness scaling factors in preset scenes, wherein the preset scenes comprise static, sitting, walking or public transportation;
And fitting the brightness scaling factor and the mathematical model of the video quality score by using a linear regression minimization MSE according to the video quality score, and obtaining the second association model.
In practice, as shown in fig. 3, consider that the color space of a video is in YUV format, including a luminance component Y and two chrominance components U and V, where the Y luminance component controls the luminance of the video color. And obtaining video quality scores correspondingly input when the testers watch videos coded by different brightness scaling factors in a preset scene, and then fitting the brightness scaling factors and mathematical models of the video quality scores by using a linear regression minimization MSE according to the video quality scores to obtain the second correlation model.
First, the collected test video may be scaled by a ffmpeg tool with different luminance scaling factors, such as luminance scaling factor set a= {1.0,1.1,1.2,1.3,1.4,1.5}. The encoded test video is then transmitted locally to the mobile device.
Further, the brightness of the screen of the mobile device is precisely controlled by using customized brightness adjusting software, and scaling is performed by using the same brightness scaling factor. It should be noted that the luminance component Y has a value ranging from 0 to 255, and thus scaling with the same luminance factor may cause partial distortion of the picture, as shown in the following formula
Y'＝α*min(Y/α,255)≈Y
Where α is the luminance scaling factor, Y is the original luminance of the video, and Y' is the scaled luminance. The distortion resulting from scaling is considered below in the evaluation of video quality.
Further, a plurality of testers watch the test video with scaled brightness, and record the video quality score input by each tester. The following equation is then fitted with a least squares regression to obtain the gain G b for the video quality for brightness scaling.
Gb＝c6*α2+c7*α+c8
Where α is a scaling factor and c 6,c7,c8 is an adjustable parameter that is evaluated by the user.
S103, combining the first association model and the second association model according to a preset weight factor to obtain a video quality model;
In implementation, after the first correlation model and the second correlation model are obtained, the frequencies f r and f of occurrence of the two low-quality events of buffering and bit rate switching can be calculated according to the play data collected by the customized video player by using the following two formulas b
fb＝(bi-1-bi)+
Wherein S i represents the size of the i-th video block, R i represents the detected throughput, B i represents the size of the buffer, B i represents the bit rate of the i-th video block, the positive sign indicates that the value in brackets is taken if the value in brackets is equal to or greater than 0, otherwise, 0 is taken.
Further, the frequencies of buffering and bit rate switching are reduced to the variable I t as a loss value of video transmission quality using the following equation
It＝fr*wr+fb*wb
Where w r and w b are parameters that weigh the specific gravity of the two, and can be adjusted according to the actual user requirements, for example, if the user prefers to play coherent video, the value of w r is larger, and if the user prefers to play high-bit-rate video, the value of w b is larger.
Further, the original quality score Q of the video without vibration and brightness scaling can be obtained according to the bit rate of the video and the user evaluation by using the following formula o
Where c 1,c2 is two tuning parameters and b is a particular coding bit rate of the video.
Further, several quality estimations described above may be combined to obtain the following formula
Q＝Qo-It-Iv+Gb
Where Q o is the original mass without shaking and luminance scaling, I t is the mass loss due to the transmission process, I v is the mass loss due to the scene of different shaking intensities, and G b is the mass gain obtained by luminance scaling. So far, several video quality evaluation indexes with different dimensions are combined together to obtain the video quality model.
S104, measuring the power consumption condition of the mobile equipment for transmitting and playing the video stream under different screen brightness, and establishing a relation function between the brightness and the power consumption according to the power consumption condition to obtain a power consumption model;
further, in step S104, the power consumption situation that the mobile device transmits and plays the video stream under different screen brightness is measured, and a relation function between brightness and power consumption is established accordingly to obtain a power consumption model, which includes:
respectively measuring the power consumption of the mobile equipment for transmitting and playing video streams under different screen brightness until reaching a preset duration;
and according to the power consumption, obtaining the power consumption function model through linear regression minimization MSE fitting.
In particular, as shown in fig. 4, the power consumption of the mobile device in transmitting video can be formed as the following binary quadratic equation
Etran(n)＝c9*s2+c10*s+c11*b2+c12*b+c13
Where s is the current signal strength of the mobile device, b is the bit rate of the video block currently to be transmitted, c 9,c10,c11,c12,c13 is the tuning parameter, and the best parameter is fitted for the specific device and use scenario.
Further, the power consumed by the mobile device in the process of rendering the display video is formed as the following formula
Edisp(n)＝fdisp(α)*T
Where f disp (α) is a non-decreasing function of the scaling factor α and T is the duration that the device actually plays the video.
Further, the two components of the power consumption are combined to obtain the following formula,
E(n)＝Etran(n)+Edisp(n)
Where n represents the sequence number of the video block.
S105, multiple linear regression fitting is carried out on the video quality model and the power consumption model to obtain an optimization target;
optionally, the expression of the optimization target is Where E (n) represents the power consumption of the nth video block, E max is the maximum value of the measured power consumption, Q (n) represents the video quality score of the nth video block, Q max is the maximum value of the measured video block quality, and γ is a parameter controlling the weight of both.
In particular, after the video quality model and the power consumption model are obtained, the evaluation index Q of video quality and the power consumption E can be combined to obtain a deterministic optimization target as the following formula
Where E (n) represents the power consumption of the nth video block, E max is the maximum value of the measured power consumption, Q (n) represents the video quality score of the nth video block, Q max is the maximum value of the measured video block quality, and γ is a parameter controlling the weight of both. For example, in a scene of stable power in a room, a smaller gamma value can be tried to represent less concern about electricity consumption, so as to obtain better video quality. In the outgoing scene, a larger gamma value can be selected to represent the power consumption, so that the purpose of saving power is achieved, an optimal balance point between power consumption and video quality is achieved, and the setting can be specifically carried out according to actual requirements.
And S106, calculating the optimal decision of the bit rate and the brightness scaling factor of the mobile device according to the optimization target.
Optionally, before calculating the best decision of the bitrate and brightness scaling factor of the mobile device according to the optimization objective, the method further includes:
Sampling the flow in a preset time range, calculating a sliding window average value, obtaining estimated throughput and adding the estimated throughput to the optimization target.
Based on the above embodiment, considering that the higher the bit rate of the video block is, the larger the required bandwidth is, the throughput of the last period of time can be used to estimate the network bandwidth of the next time, the flow in the preset time range is sampled, the average value of the sliding window is calculated, the estimated throughput is obtained and added to the optimization target, and the estimated throughput can be used as the reference of the bit rate decision of the next video block.
Further, according to the optimization objective, the calculating the best decision of the bitrate and brightness scaling factor of the mobile device in step S106 includes:
And optimizing the optimization target by utilizing an optimizer to obtain the optimal decision of the bit rate and the brightness scaling factor of the mobile equipment.
When the video player is embodied, when transmitting a video stream and making a bit rate decision, the video player solves an optimization problem aiming at the formula in a certain range of sampling time, and utilizes an optimization controller to carry out numerical analysis and calculation on an optimization target in the following formula to obtain the optimal bit rate and brightness scaling factor of a currently required video block, and then requests to download a corresponding video block, and transmits and locally renders the video block. And the like, completing the transmission rendering and playing of the whole video.
According to the video stream transmission adaptability and energy saving method, the scores of the testers are collected, the existing brightness scaling mechanism is directly related to user perception, the power consumption factors and the video stream watching quality are formed into a deterministic optimization problem, and then the optimal decision is generated, so that the video stream transmission adaptability and energy saving performance are improved.
The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the disclosure are intended to be covered by the protection scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (7)
1. A method of mobile device video streaming based on brightness scaling, the method comprising:
respectively obtaining video quality scores of different testers under vibration grades corresponding to different scenes, and establishing a first association model according to the video quality scores;
the step of respectively obtaining video quality scores of different testers under vibration levels corresponding to different scenes and establishing a first association model according to the video quality scores comprises the following steps:
obtaining video quality scores correspondingly input when the testers watch videos with different bit rates in preset scenes, wherein the preset scenes comprise static, sitting, walking or public transportation;
The vibration monitoring software is adopted to obtain the accelerations a m,x,am,y,am,z in three directions in the video stream transmission process by using an embedded acceleration sensor of the mobile phone, the accelerations are processed to remove the gravitational acceleration of the earth, the average motion m a in a sampling window is calculated according to the following formula,
Wherein M is the size of the sampling window;
The average change rate f a in the sampling window M is calculated as follows
The vibration level v in the sampling time range is calculated by the following formula
v＝β*ma+(1-β)*fa
Wherein β is an adjustable parameter that controls the weight of the two variables;
Fitting a mathematical model of the vibration level and the video quality score with a linear regression minimization MSE according to the video quality score to obtain the first correlation model;
Respectively obtaining video quality scores of different testers under brightness scaling factors corresponding to different scenes, and establishing a second association model according to the video quality scores;
Combining the first association model and the second association model according to a preset weight factor to obtain a video quality model;
Measuring the power consumption condition of the mobile equipment for transmitting and playing the video stream under different screen brightness, and establishing a relation function between the brightness and the power consumption according to the power consumption condition to obtain a power consumption model;
Multiple linear regression fits the video quality model and the power consumption model to obtain an optimization target;
and calculating the optimal decision of the bit rate and the brightness scaling factor of the mobile device according to the optimization target.
2. The method of claim 1, wherein the step of separately obtaining video quality scores of different testers under the brightness scaling factors corresponding to different scenes and establishing the second association model according to the video quality scores comprises the steps of:
obtaining video quality scores correspondingly input when the testers watch videos coded by different brightness scaling factors in preset scenes, wherein the preset scenes comprise static, sitting, walking or public transportation;
And fitting the brightness scaling factor and the mathematical model of the video quality score by using a linear regression minimization MSE according to the video quality score, and obtaining the second association model.
3. The method according to claim 1, wherein the step of measuring the power consumption of the mobile device for transmitting and playing the video stream at different screen brightnesses, and establishing a relation function between brightness and power consumption according to the power consumption, to obtain the power consumption model comprises:
respectively measuring the power consumption of the mobile equipment for transmitting and playing video streams under different screen brightness until reaching a preset duration;
And according to the power consumption, obtaining the power consumption model through linear regression minimization MSE fitting.
4. The method of claim 1, wherein the expression of the optimization objective isWhere E (n) represents the power consumption of the nth video block, E max is the maximum value of the measured power consumption, Q (n) represents the video quality score of the nth video block, Q max is the maximum value of the measured video block quality, and γ is a parameter controlling the weight of both.
5. The method of claim 1, wherein prior to the step of calculating an optimal decision for the bitrate and brightness scaling factor of the mobile device based on the optimization objective, the method further comprises:
Sampling the flow in a preset time range, calculating a sliding window average value, obtaining estimated throughput and adding the estimated throughput to the optimization target.
6. The method of claim 1, wherein the step of fitting a mathematical model of the vibration level and the video quality score with a linear regression minimized MSE based on the video quality score, results in the first correlation model, the method further comprising:
And acquiring acceleration data in a preset sampling period, and obtaining the vibration grade according to the acceleration data.
7. The method of claim 1, wherein the step of calculating an optimal decision of the bitrate and brightness scaling factor of the mobile device based on the optimization objective comprises:
And optimizing the optimization target by utilizing an optimizer to obtain the optimal decision of the bit rate and the brightness scaling factor of the mobile equipment.
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