Display Content Change Rate Analysis for Power Saving in

Transmissive Panels

Raghu Bankapur

, Krishna Kishor Jha

and Vaisakh P. S.

Technical Lead, Multimedia & Systems Division, Samsung R&D Institute India Bangalore Pvt. Ltd, 16/1, 8th Cross,

Muni Reddy Layout, Mahadevapura, 560048, Bangalore, Karnataka, India

Technical Lead, Multimedia & Systems Division, Samsung R&D Institute India Bangalore Pvt. Ltd, #303, Thirunaga

Wings, 2nd Cross, Byrasandra, C V Raman Nagar, 560093, Bangalore, Karnataka, India

Technical Manager, Multimedia & Systems Division, Samsung R&D Institute India Bangalore Pvt. Ltd, 2870, Phoenix

Building, Bagmance Constellation Business Park, Doddenakundi, 560037, Bangalore, Karnataka, India

Keywords: Backlight Control, Color Distribution Coefficient, Content Change Rate Coefficient, Luminance, Panel

Tuning Lookup Table, Self-Organizing Map.

Abstract: Energy conservation to protract battery life is a major challenge for sustained richer user experience in

mobile devices. This paper presents Content Change Rate Coefficient (CCRC) and image Color Distribution

Coefficient (CDC) based method to govern backlight scaling and image luminosity adjustment to reduce

power consumption in backlight module. The existing methods intending to achieve similar results have

significant shortcomings like inter-frame brightness distortion, flickering effects, clipping artifacts, color

distortion. These problems are addressed in proposed technique and user's visual perception is not

compromised while ensuring image fidelity (less than 6.2% degradation) and reduces nearly 80mA current

consumption. Proposed method derives Aggressiveness Coefficient from color distribution and content

change rate to address above problems. Method is prototyped on Samsung Galaxy Tab AS and Galaxy

Mega2 and experimental results clearly show 16% reduction in current consumption and 88% reduction in

flickering artifacts, which is active phone usage time getting extended by ~1hr for an average usage of

20hrs.

1 INTRODUCTION

Users of portable consumer electronics products,

such as mobile phones, tablets, GPS etc. reckon to

operate these devices for a longer period of time. So

these product manufacturers always strive towards

constantly optimizing their power consumption to

meet this objective.

Transmissive panel is one of the most common

Liquid Crystal Display (LCD) screen, which

requires a backlight as the light source and there is

no reflective film in the back of LCD screen. These

types of LCDs are preferred over other display

technologies in wide range of consumer products

because of low cost, manufacturing ease, light

weight, high resolution, good color performance and

other advantages. However, there are some

downsides i.e. low image contrast ratio and constant

power consumption irrespective of image displayed.

In typical LCDs, the image is displayed by

controlling the LC transparency with constant

backlight luminance. The image contrast ratio for

such display panels is impoverished because the

light is not blocked completely at the lowest gray

level. In LCD based products power is primarily

attributed to backlight, nearly 90% (Carroll and

Heiser, 2010); because the backlight always operates

with the constant luminance regardless of input

image and content based backlight control is

considered as one of the most effective technique for

power saving in LCD panels. Several methods are

proposed in the literature (Tsai et al., 2009;

Kerofsky and Daly, 2006; Chen et al., 2013; Choi et

al., 2002; Raman and Hekstra, 2005; Cheng et al.,

2004; Hsiu et al., 2011) that leverage this and reduce

power consumption. However, they are prone to

multiple artifacts (Hsiu et al., 2011) like inter-frame

brightness distortion, frequent flickering effects,

clipping at saturated pixels, color distortion etc. due

to irresolute operation.

446

Bankapur, R., Jha, K. and S., V.

Display Content Change Rate Analysis for Power Saving in Transmissive Panels.

DOI: 10.5220/0005733404460456

In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pages 446-456

ISBN: 978-989-758-173-1

The present paper proposed a unique method to

effectively overcome these defects comparable to

conventional methods, while maintaining the richer

user experience. Most importantly, proposed method

conserves power. In particular we make below three

contributions addressing following research

questions.

First, how to minimize inter-frame brightness

distortion and frequent flickering effects? A Content

Change Rate based Coefficient (CCRC) is derived to

restrict frequent backlight modifications to reduce

flickering effects and inter-frame distortion by 88%.

This is presented in section 3.1, 4.2, 5.2.

Second, how to minimize color distortion and

clipping at saturated pixels? An image classification

model to classify image into color rich and simple

UI with 93% accuracy is modelled. The pixel level

modifications to displayed image are based on this

classification to ensure minimum color distortion.

This is presented in section 3.2, 5.1.

Third, how to maximize power saving without

compromising user experience? A content change

rate and color distribution coefficient based

algorithm is modelled to achieve high power saving

when content on the display is Simple UI (not color

rich) and less dynamic. This is presented in section 4.

User study shows that more than 25% of content on

display is less dynamic. Also the present algorithm

saves 16% power with more than 93.8% Structural

SIMilarity (SSIM) (Wang et al., 2004) accuracy.

The experimental results for proposed method are

discussed in section 5.

Figure 1 shows block diagram of proposed

technique. It consists of revised display subsystem

with an additional content change and color

distribution analysis block and backlight and image

intensity control block. Content change analysis and

color distribution analysis block quantifies Content

Change Rate Coefficient (CCRC) and Color

Figure 1: Block diagram for proposed technique.

Distribution Coefficient (CDC) respectively and

other block modulate image intensity and backlight

level based on CCRC and CDC.

2 LCD AND PERCEIVED

LUMINANCE

Before stating algorithm, it is necessary to

understand relation between brightness, backlight,

power consumption and intensity of image. For LCD

based products, major power consumption is

attributed to backlight and it is the main source for

brightness of the LCD, which is measured in lux.

There are different techniques to control brightness

of display such as backlight and intensity.

The optical light flow in LCD subsystem (Raman

and Hekstra, 2005) is illustrated in Figure 2, where

in backlight is main source of light (Backlight

Luminance BL) and fragmentary directed light pass

through pixel depends on intensity value.

Figure 2: Transmissive property of TFT LCD.

The perceived intensity of an image is denoted by

(1)

I = βLY

(1)

Where β is the transmittance of LCD Panel, L is

backlight luminance and Yfr is the average

luminance value of the frame (Raman and Hekstra,

2005). ‘L’ can be controlled linearly by Backlight

Level (BL) and the ‘Yfr’ can be controlled by

increasing/decreasing the Luma component for the

frame called Image Intensity Level (IL). Concurrent

adjusting of intensity and backlight level in proper

way leads to energy conservation, but just modifying

these above mentioned parameters may cause

defects in displayed image.

So there are two parameters that contribute to the

final luminance and hence, same brightness

perception can be produced by multiple

combinations of backlight and pixel’s intensity value.

Multiple experiments were done to find relation

between perceived brightness, backlight level, image

intensity along with differences in power consumed.

Figure 3 shows brightness variation w.r.t backlight

Display Content Change Rate Analysis for Power Saving in Transmissive Panels

447

level at multiple intensity of image. From this figure,

it’s evident that same luminance level can be

achieved from a range of backlight levels and

modifying image intensity. For instance, brightness

value of 100 lux can be achieved either by backlight

level 250 or 130 with intensity increase from 0(I 0)

to 25(I 25).

Figure 3: Brightness Vs Backlight Level at different

Intensity level.

Power consumption variation with respect to

increase in backlight level and intensity of image is

further explained in figure 4. While power

consumption is directly proportional to backlight

values, increase in pixels intensity has negligible

impact, less than 5mA. For instance, reducing

backlight level from 250 to 130, there is around

110mA power reduction. And as illustrated in Table

1 for luminance values of 100, 75 and 50,

considerable power can be saved by manipulating

pixel intensity and backlight simultaneously,

ensuring similar perceived brightness. For instance,

100lux luminance intensity can be produced from

device by any pixel intensity and backlight level

combinations like (250, 0), (205, 4), (170, 8) etc. And

all these combinations drain different amount of

currents. Like, by reducing backlight level from 250

to 170 and increasing intensity value from 0 to 8,

75mA current consumption is reduced without

degrading image perception to the eye.

It is shown that controlling backlight and

intensity concurrently lead to power benefit,

however these techniques suffer from drawbacks

like inter-frame brightness distortion, flickering

effects, clipping artifacts, color distortion etc. This

present work tries to redress these associated

drawbacks and suggests a novel method of content

change rate and color distribution based backlight

and intensity control. Significance of content change

and color distribution analysis is discussed in next

section.

Figure 4: Power consumed vs Backlight level at different

intensity level.

Table 1: Luminance at different intensity and Backlight

level.

Luminance

Backlight

Level(BL)

Intensity

Level(IL)

Current

consumption

100

250 0 330

205 4 280

170 8 255

210 0 305

175 4 258

150 8 235

150 0 235

130 4 220

100 8 190

3 KEY CONCEPTS IN

PROPOSED METHOD

This section discusses about two of the main

inventions done during this research work. First one

is display content analysis which is prime discovery

done to avoid defects like inter frame flicker and

distortion and second is avoidance of color distortion

for color rich images. Next sub sections discuss

detail about these inventions.

3.1 Display Content Change Analysis

and Its Significance

Display content adjustment and backlight control is

done dynamically based on image characteristics.

This is because every image has different color

distribution and any such adjustment of pixels

intensity in saturated images will lead to quality

degradation and clipping artifacts.

In conventional techniques, for use cases where

screen is changing at 30fps or continuously updating,

intensity and backlight adjustment coefficient is

calculated on per frame basis and on every frame

ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods

448

backlight is adjusted by different amount. As a result,

flicker and inter frame distortion become prominent

and thus resulted in bad user experience.

The study on the use cases where Display

content is static with over 50 users was done. It was

found that, on an average in more than 25% of use

time, frame is idle or display updates at low

frequency. Also in these use cases limited color

variations are present. Like during call when speaker

mode is ON, or while reading Pdfs books,

messaging etc., display is static for majority of time

and also contents have limited color distributions. In

these cases per frame backlight control is not

required and hence flickers and distortion can be

avoided by gradual backlight adjustment.

Figure 5 shows display content change analysis

for different application installed on smartphones

obtained from study performed. It was found that for

major applications like launcher, contacts, browser,

email etc. screen was static for more than 20% of

time. So, selectively implementing this algorithm in

use cases limited by static condition can still have

huge power saving together with rich user

experience.

Figure 5: Statistics of Display content change rate. Note

that this data taken when there are no frame submissions

for duration of 2s.

Display content change analysis can be explained

in terms of Content Change Rate (CCR) that is

derived based on frame submission rates to display.

For use cases like video play, gaming applications

CCR will be high as content in such cases changes at

high rate and incase of launcher, contacts, call etc.

CCR will be low. Hence content change rate

analysis is done to calculate CCR intensity based on

which image intensity adjustment and backlight

control is performed.

There are cases wherein displayed images are

having saturated colors, thus change in intensity may

cause image distortion and also in such cases

intensity has negligible effect on luminance. So in

proposed technique, detecting such use cases and

applying final intensity based on color distribution

analysis is shown; discussed in next section.

3.2 Color Distribution Coefficient

(CDC)

Proposed algorithm attempts to minimize the image

distortion in case of images which are color rich, and

at the same time tries to save more power on image

which are simple UI. Thus image on display is

classified into two types- Color Rich and Simple UI.

Color Rich images have wide color distribution

whereas Simple UI consist few colors. For instance

UI of Alarm Clock, Contacts Application, etc. are

Simple UI images and photos/videos are color rich

images.

Self-Organizing Map (SOM) (Schatzmann and

Ghanem, 2003; Kohonen, 1990) based technique is

used for this classification. The SOM is

unsupervised neural network based dimension

reduction technique. Each node in the SOM has a

weight vector whose dimension is equal to the

dimension of input data. Rectangular SOM can be

visualized as a rectangular mesh of n x m nodes (e.g.

Figure 10 shows 3 x 3 rectangular SOM). The length

of line connecting the nodes is Euclidean distance

between the weights of Nodes. SOM exhibits a

property that it maintains the original data topology,

i.e. similar items get mapped to nearby nodes. As a

result of this topology preservation, when SOM is

trained with the input data it tries to take the shape

of data distribution. For example consider an

imaginary image whose pixel values, when plotted

in 3D scatter plot, the plot takes the shape of sphere.

If a rectangular SOM is trained with this image pixel

values as input, the rectangular SOM mesh plot will

take the shape of sphere too. This property of SOM

is harnessed here to make the color distribution

estimation. A 6 x 6 rectangular SOM is created

which is trained with pixel values of input image.

Training algorithm for the SOM is described as

below (Kohonen, 1990).

1. Initialize the Weight vectors W(I, J) with random

values.

2. Determine the winner neuron Wc(t) for input

pixel x of the image using Euclidean distance

formula.

3. Update the weight vectors using (2)

(t+1) = W

(t) + α(t)h

(t)[x(t) – W

(t)] (2)

Where α is learning rate and

is a neighborhood

Gaussian function given by (3)

Display Content Change Rate Analysis for Power Saving in Transmissive Panels

449

= exp(−







(



)



)

(3)

In above equation

−



is the distance

between the position(x, y) of winning node and i

node in the rectangular SOM map.

The neighborhood radius (o(t)) and learning rate

(α (t)) also decays with time, with below decay

function (4).

L(t) = L

exp(-





)

(4)

Where N is total iteration count, and t is current

iteration number.

4. Repeat steps 2 and 3 until total iteration count is

reached.

Fig. 8 shows the 3D scatter plot of color pixel values

(RGB) in Color rich image (Fig. 6). It also shows the

SOM (black mesh) which is obtained after training.

It can be seen how the SOM has taken the shape of

input distribution (green dots). Also a similar map is

constructed for a Simple UI image (Fig. 7) shown in

Fig. 9. It can be seen in SOM of Fig. 9 it is mostly

concentrated in one region and considerably lesser

stretched as it has less number of colors. Whereas in

Figure 8, the SOM is stretched more showing that

the distribution of color in Fig. 6 is more. A similar

observation is made with a set of 1000 images in

which 500 were simple UI ones- will be discussed in

results section.

Figure 6: Color Rich Image. Figure 7: Simple UI.

Figure 8: SOM for Colo

rich Image.

Figure 9: SOM for Simple UI.

Based on this, it is inferred that the stretch of the

SOM correctly represents the color distribution in

the image. This observation of difference in Color

distribution and hence the spread of SOM, is used as

an objective criteria to classify Color rich and

Simple UI images. The Color Distribution Metric

(CDM) can be derived from (5)

=

∑





∑





(I,J) + D

(I,J)+ D

(I,J)+

(I,J)

(5)

Where D

are the Euclidean distance of

weights in SOM from node (I,J) in SOM map from

left, top, bottom and right node respectively. Figure

10 shows the location D

of nodes in

SOM.

Figure 10: 3 x 3 Rectangular SOM showing position of

nodes of (5).

CDM represents the amount by which the SOM

map is spread and hence a high value of CDM

implies color rich image and whereas low represents

less number of colors and hence Simple UI.

CDC is derived from CDM using (6), T



is the

CDM threshold for Color Rich image. The value of



is determined experimentally as it depends on

the resolution of the image.

CDC = 

1–







≤



0>



(6)

Performance: In order to reduce the running time of

the proposed CDC calculation the display image is

scaled down to 180x320 size. Since the primary

purpose is to determine the color distribution scaling

down the image still retains its color property.

Average running time for CDC computation was

measured to be 7ms.

This CDC value is used in the algorithm

explained in next section to compute the

aggressiveness of the algorithm.

ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods

450

4 PROPOSED METHOD

As explained in section 2, same luminance can be

achieved at lower power consumption by

concurrently reducing backlight level and increase in

intensity. This section discusses about proposed

algorithm that explains when and by what amount

the intensity and backlight values need to change. It

also explains use of Color Distribution Coefficient

(CDC) and Content Change Rate (CCR) to minimize

the artifacts occurring due to change in backlight

and intensity level.

Figure 11 shows algorithm flow for proposed

technique. Algorithm starts with loading panel tuned

look up table when device boots. On frame update

Content Change Rate Coefficient (CCRC) and CDC

is computed. User is provided with authority to set

Aggressiveness ( 



) based on power saving

requirement. During this research we invented

Effective aggressiveness (



) formula which is

function of CCRC, CDC and 



. Based on

effective aggressiveness new IL and BL are

calculated and applied to system. In following sub-

sections each of these blocks are explained in detail.

4.1 Generating Panel Tuned Look-up

Table (LUX_TABLE)

The method to create lux table is depicted in Figure

12. Creating LUX_TABLE involves below two

steps-

Step 1: Display uniform gray image on display for

tuning brightness table. Gray Image is taken for

readings since RGB distribution is uniform in gray

color image.

Step 2: Measure Luminescence (Lux) for all

backlight levels from bl=0 to bl=255 and Intensity

Levels varying from il=0 to il=25 and fill

LUX_TABLE [255][25] table accordingly. For

backlight level bl=0 and Intensity Level il=0

measured lux value LUX_TABLE [0] [0] is filled in

the table as shown in Table 2.

Table 2: Lux mapping table.

Intensity

Backlight

il=0 il=1 … il=25

bl=0

LUX_TAB

LE [0][0]

LUX_

TABLE

[0][1]

…

LUX_

TABLE

[0][25]

…… ….. ……. … ….

bl=255

LUX_

TABLE

[255][0]

LUX_

TABLE

[255][1]

…

LUX_

TABLE

[255][25]

Figure 11: Flowchart for proposed technique.

Figure 12: Generating LUX_TABLE.

Setup for generating Panel Tuned look-up table

consists of Display Color Analyzer (Konica Minolta

CA-310) and a laptop with tuning tool installed. Test

device is kept in closed environment (Black box) to

avoid surrounding luminance interference as shown

in Figure 13.

Tuning tool enables mapping different backlight

levels and intensity levels and Color analyzer

enables measurement of amount of light coming out

from test device. Luminance recorded from color

analyzer is tabulated to use in proposed algorithm.

Display Content Change Rate Analysis for Power Saving in Transmissive Panels

451

Figure 13: Setup for generating panel tuned look-up table.

4.2 Computation of Content Change

Rate Coefficient (CCRC)

As discussed in section 3.1, significance of content

change analysis with reference to power saving and

quality assessment is very important parameter to

consider. The shortcomings in existing methods like

inter-frame brightness distortion, flickering effects,

clipping artifacts, color distortion is fixed by using

content change rate. Let’s consider video play use

case where content considered being high quality

and Content Change Rate (CCR) is min 30fps.

Existing work in this domain didn’t consider CCR.

Intensity and backlight change is applied on per

frame basis, causing inter-frame brightness

distortion. So in proposed technique CCR is

considered as one of decisive parameter for power

saving to eliminate these artifacts.

CCR is calculated based on number of frames

updated per unit time measured in frames per second.

CCRC calculation for different CCR is shown in

Table 3.

Table 3: CCRC computation based on CCR.

SL No. CCR CCRC

1 More than 60 0

2 45 to 60 1

3 30 to 44 2

4 20 to 29 3

5 10 to 19 4

6 8 to 9 5

7 6 to 7 6

8 4 to 5 7

9 2 to 3 8

10 1 or less 9

4.3 User Specified Aggressiveness

Proposed algorithm gives flexibility to user for

manually making algorithm more aggressive in

power saving, in case he is comfortable with slight

image degradation. This can be very useful when

user is running out of battery. Table 4 shows

representation of user aggressiveness based user

power level selection.

Table 4: Representation of user aggressiveness based on

power saving level.

SL No. User power saving level





1 low 0.5

2 mid 0.7

3 high 1

4.4 Computation of Effective

Aggressiveness

Effective Aggressiveness (



) including CCRC,



and CDC can be derived as (7)





= CCRC *max(CDC,A



))

(7)

Where

• 0 <= 



<= 9

• 0 means no power saving

• 9 means highest power saving

Here max of CDCand A



is considered since

effective power saving should be max. Use of A



will be shown in next section.

4.5 Computation of New Intensity Level

(IL) and Backlight Level (BL)

The method of finding out new BL and IL using



and LUX_TABLE is described as below:-

Step 1: Read the current back-light level and find the

luminance (lux) at this back-light level (say BLc) by

indexing into the LUX_TABLE.

LUXc = LUX_TABLE (BLc, 0);

Step 2: Search for luminance (LUXc) in the

LUX_TABLE for all back-light level less than the

current backlight level(BLc) and higher intensity

levels as per below matching function to create

LUXMAP_ARRAY.

LUXMAP_ARRAY (BL, IL) = search (LUXc, threshold,

LUX_TABLE);

Where

• For all BL < BLc and IL > 0

• search(X, threshold, T(x,y)) is a function which

returns all sets of {x,y} for which the value

• T(x, y) = X ± threshold.

• This generates array of BL, IL pairs, producing

same amount of lux.

As explained in section 2, same lux can be produced

with different combinations of Backlight and

Intensity. Hence LUXMAP_ARRAY is array of BL

and IL pairs which produce same LUXc.

Display Color

analyzer

Black

Box

ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods

452

Step 3: Select the {BL, IL} pair from

LUXMAP_ARRAY by indexing the array at A

eff

, as

shown in below equation

[BLn, ILn] = LUXMAP_ARRAY [A

eff

]

Where

• BLn is new backlight level to be applied

• ILn is new Intensity level to be applied

New calculated BLn and ILn levels are applied to

backlight subsystem and display frame to get better

power saving without compromising on the quality.

5 EXPERMENTAL RESULTS

AND DISSCUSSION

5.1 Color Distribution Coefficient

Color Distribution Coefficient (CDC) was computed

for a set of 500 Simple UI and 500 Color rich

Table 5: SOM based CDC computation for different

images.

Image

SOM

Plot

CDM CDC Image

SOM

Plot

CDM CDC

81 0

27 0.46

65 0

39 0.22

93 0

44 0.12

82 0

23 0.54

67 0

37 0.26

Parameters selected for SOM are

Network Size = 6 x 6 Initial Neighbor Hood Radius = 6

Initial Learning Rate = .9 CDM Threshold (Th) = 50

Iteration Count = 32768

images. It was observed the computation correctly

classified nearly 93% of the test images. Also for

images with saturated colors, CDM was observed to

have higher value; hence this approach minimizes

the image modification to images having saturated

colors. Which can be justified since the image with

saturated colors have higher pixel values and so

stretch in SOM also becomes more with clusters

having lower pixel values.

Table 5 shows the value of CDM and CDC

computed using concept explained in section 3.2. It

can be observed that images having higher color

distribution have more stretch in SOM and hence

resulting in higher CDM value.

5.2 Content Change Rate Analysis

The proposed method was deployed on Galaxy

Mega Smartphone and Samsung Galaxy Tab AS and

usage data for multiple users for a period of 30 days

were recorded. Usage data is depicted in Table 6.

Table 6: Application wise display static percentage (> 2s)

for multiple users.

Application User 1 User 2 User 3 User 4

Call, contact

40% 35% 31% 29%

Facebook

22% 27% 23% 19%

61% 13% 33% 27%

Launcher

19% 35% 23% 29%

Gallery

16% 8% 13% 17%

Average

31% 23.6% 24.6% 24.2%

Attempt was made to record the duration of static

content displayed while normal uses. So that

estimate for power saving can be done. It is seen in

Table 6; all popular applications have significant

static content.

Hence it can be inferred that there are many use

cases which have lower content change rate. So

there is a huge scope of power saving in algorithm,

since it is directly proportional to CCRC. The

algorithm saves power even in cases which have

high content change rate (low CCRC) but relatively

savings will be less.

5.3 Power Saving Analysis

Power saving for proposed technique is evaluated on

monsoon power monitor setup as shown in Figure

14. Power number measurements are done with and

without proposed technique for multiple use cases of

mobile device. Current consumption waveforms is

shown in Figure 15, showing around 80mA less

current consumption during static content use case

with the proposed solution.

Display Content Change Rate Analysis for Power Saving in Transmissive Panels

453

Figure 14: Power measurement setup.

After applying proposed technique on Galaxy

Mega 2 Smartphone, Samsung Galaxy Tab AS, test

shows positive results, with a current saving of

58mA to 80mA as shown in Figure 16. Power

savings was recorded for static use cases with

Simple UI image, to get an estimate of the maximum

savings.

Figure 15: Current consumption data with and without

proposed technique for browser use case.

Figure 16: Power saving data with and without proposed

technique.

Average power saving recorded with different

aggressiveness value on 4 devices is shown in

Figure 17. It can be observed that the amount of

power saving almost grows linearly with

Aggressiveness which is based on quality of image

and the rate at which the content is changing. Thus

least power saving achieved when, user display is

occupied with color rich image or a highly changing

content. When screen is having slow changing

content and simple UI, power saving is very high.

Figure 17: Average power saving for different

aggressiveness.

Figure 18, further shows the variation in

aggressiveness when user is using his phone in

launcher, contacts and video applications. It can be

seen when there is slow changing content (High

CCRC) and less color rich (high CDC) the

aggressiveness value is high and hence the power

savings.

Figure 18: Variation of 



with respect to CCRC and

CDC.

As per the experimental results obtained in

Figure 16 for idle use cases, it can be inferred that

25% of the time device is in idle condition and

correspondingly 16% power saving can be achieved

in this condition. So effectively 4% power saving is

achieved throughout. It can be represented as

~50min increase in device usage time, if the device

is used for 20hrs. This increase in device usage time

is minimum power saving ensured by proposed

algorithm. Since calculation done above is only for

static content (i.e. CCRC = 9), but since algorithm is

operational and saves power in cases of slowly

changing content also it is bound to produce more

power saving.

Test Device

Power monitor

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Further section discusses the quality assessment

of the proposed algorithm.

5.4 Quality Analysis

Quality analysis for different intensity and backlight

levels are evaluated based on Structural SIMilarity

(SSIM) (Wang et al., 2004). Figure 19 shows

experimental set-up, which consists of a mounted

Full HD Smartphone device to capture images of

test device with proposed algorithm running. Test

device is placed in closed container to avoid

surrounding light interference and test device is

connected to laptop via USB and controlled using

Android Debugger Bridge (ADB) command shell

interface.

SSIM analysis is done for different use cases at

different aggressiveness and analysis results are

depicted in Figure 20. Distortion due to proposed

algorithm is found to be negligible. Comparative

Figure 19: Experimental setup for Quality analysis.

Figure 20: Display image captured at different

aggressiveness.

analysis of image quality evaluated for different

aggressiveness with reference to luminance, contrast

and structure. Figure 21 shows quality analysis, the

error in contrast and luminance is very negligible

compared to structural index. Comparative study of

the image degradation results of presented algorithm

with the existing art (Hsiu et al., 2011) in this

domain reveals the proposed method achieves least

degradation.

Figure 21: Quality analysis for different aggressiveness.

Test application was made to invoke most used

applications, and flicker counts were recorded. It

was found that proposed method reduces flickering

artifacts by 88%. Results of same are shown in

Table 7.

Table 7: Comparison of Flicker counts of proposed and

conventional methods.

Use cases

Flickers recorded on Galaxy Tab AS

Proposed Conventional

Browser 1 6

Message 0 2

Launcher 0 3

Facebook 1 5

Fig. 22 shows the variation of PSNR along with

amount of power saving achieved. This is aligned

with our expectation as color rich image will have

more distortion and hence high PSNR and low

power saving. Also the more distortion we allow the

better power we can save due to lower level of

backlight. Average PSNR of TW-CES and (Kim et

al., 2010) was 26.32 dB and 27.11 dB respectively

and that of the proposed method was 29.05 dB

during high power saving. Hence the results show

the proposed method can extend the battery life by

saving power consumption at the same time

maintaining better user experience.

Black

Box

USB connector

for test device

USB connector

for camera

Display Content Change Rate Analysis for Power Saving in Transmissive Panels

455

Figure 22: PSNR measurement for different power saving.

6 CONCLUSIONS

The paper proposed a technique which is based on

Display content change analysis; Color distribution,

Backlight scaling and Intensity compensation to

achieve power saving in LCD class of displays. The

paper presented a 93% accurate image classification

method using Self Organizing Map to compute the

color distribution coefficient, which is used to

reduce chromatic distortion. It also showed

significance of low content change rate in mobile

devices and how they can be used to eliminate the

problems like inter-frame brightness distortion and

flickering by 88%, which are common problems for

all image luminosity compensated backlight scaling

methods. The proposed algorithm is prototyped on

Samsung Galaxy Tab AS and Galaxy Mega 2 and

power saving which is illustrated is encouraging.

The Structural similarity metric is used as image

distortion metric to evaluate the image distortion

which was found to be negligible and not

perceivable by human eye. The results show the

proposed method can extend the battery life

significantly by saving power consumption while

maintaining good user experience.

In future CDC computation algorithm will be

enhanced. Also contrast modification along with

intensity modification can improve the visual quality

more better which will be evaluated in the further

work of this research.

ACKNOWLEDGEMENTS

The authors would like to thank Mahammadrafi

Maniyar, Rajib Basu and Dibyadarshi Debadas from

System Software Team of Samsung R&D Institute

India Bangalore for providing support in this work.

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