Kyungtae Han

Nithyananda S Jeganathan

Paul S Diefenbaugh

CiteSeerX

P-91: Parametric Evaluation of Computer Displays for Optimized Display 
Data Transmission  
Kyungtae Han, Nithyananda S. Jeganathan, and Paul S. Diefenbaugh 
Intel Labs, Hillsboro, Oregon, USA 
 
Abstract 
Display sub-system is among the highest power consuming entity 
on a computer platform and the energy efficiency of the display 
pipeline has a significant impact on the battery life, especially in 
mobile devices. Traditional display sub-systems expend a lot of 
energy to maintain the stable images on the screen by 
transmitting the entire screen contents to display even if there is 
little or no change in the screen. This paper characterizes the 
computer generated screen updates for different type of 
workloads by two independent techniques: HW & SW and 
presents an analysis of the screen updates. Our data shows that 
typically only less than 20% of the screen content changes and 
there is 20-200ms idle time period between active frames. Future 
display technologies can take advantage of this redundancy in 
screen contents to save power on the display sub-system.  
1. Introduction 
Today's computer displays consume the largest power (about 
30%) among the components in mobile platforms and thus energy 
efficiency of the display pipeline has a significant impact on the 
battery life of the mobile device [1]. Traditional displays expend a 
lot of power in maintaining the illusion of stable image by 
refreshing the panel at a fixed rate. The display power 
consumption problem is compounded by the fact that even if there 
is little or no change on the screen the platform has to generate 
and send the entire frame during refresh cycle. In order to 
improve the energy efficiency of display architecture in modern 
platforms, it might be meaningful to understand the behavior of 
the display sub-system from generation to delivery of frames for 
optimizing the pipeline for power.  
The display sub-system on PC platforms consists of a Panel, 
Framebuffer (FB) in main/graphics memory, Display timing 
controller (DTC), display link, backlight inverter and lamp. 
Though the backlight lamp and inverter consume the major 
fraction of power in the sub-system, the power consumption is can 
be considered to be relatively constant as long as the display is 
active. 
Bhowmik and Brennan [1] have discussed methods of reducing 
the backlight power and adjusting the brightness of the display in 
accordance with the framebuffer brightness and ambient light in 
Intel platforms. Choi et.al.[2] recommend adjusting the duty cycle 
of refresh to save 19% of the display power on the FB and data 
bus along with other hardware techniques for backlight power 
management. Shim et.al. [3] proposed FB compression as a 
technique to reduce the memory accesses to save power on 
displays. Ranganathan et.al.[4] collected statistics of the 
percentage of the window of focus and this information can be 
used to dim background area to save backlight power. Though the 
authors have proposed different strategies to save power on the 
sub-system, understanding the nature of the screen updates and its 
impact on the display data traffic will be fundamental to 
efficiently utilize the power management parameters in the 
pipeline. 
 
MOTION 
ANALYSIS BY SW
SW
APPLICATIONS
CPU/ GPU
FRAMEBUFFER
COMPUTING 
DEVICE
DISPLAY
PANEL
MOTION 
ANALYSIS 
BY HW
 
Figure 1. Characterization environment. 
In this paper, we characterize the display contents under diverse 
workloads to evaluate the screen updates and the frequency of 
these updates to the display.  
Our contribution in this paper includes: 
 Display characterization  [Sec. 3] 
 Analysis of motion with timing and spatial method [Sec. 4] 
 Potential methods to improve efficiency of display systems 
[Sec. 5] 
2. Display System 
Display pipeline consists of two components: computing device 
(CPU/GPU) and display sub-system. Computing device generates 
and writes the display contents to the FB. The DTC in the display 
sub-system reads a set of pixels from the FB at a time and 
transfers these contents to the display panel at a fixed rate defined 
by the pixel clock of the panel. In traditional displays, even if the 
computing device doesn't update the FB, DTC will fetch the FB 
contents and transmit the FB data to the panel. The panel will 
sustain the screen contents for a certain time only and at the end 
of its one frame display time, an interrupt will be generated 
(VBlank) to the DTC to fetch the next frame to panel. In modern 
computer platforms, this process will repeat as long as the display 
is active. 
Typically, there are static regions and images in computer display 
contents. Many applications have large sections of static regions 
and static images in a screen. Simple movement of the cursor for 
example, even though only the area equivalent to cursor changes 
in the frame buffer, typical display subsystem generates screen 
size of entire screen and transports them to the display clients. A 
video is made up of series of images called frames and for 
displaying videos, the computing device generates frames and 
sends them to display panels at a certain rate. This rate is called 
frame rate and its unit is frame-per-second (FPS).  
Similar to desktop contents even for videos, not all the video 
contents change with every frame as a new frame with different 
contents will be generated only every n frames depending on the 
FPS number. We discuss further in the following sections, our 
setup, the data collected and how this data can be useful to 
optimize the display pipeline for power. 
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3. Profiling Method 
3.1. Measurement environment 
Display profiling data is dependent on measurement environment 
including software applications and hardware configuration. Some 
of the standard benchmarks programs such as MobileMark [5] and 
SYSMark [6] run for user independent applications. Some random 
mouse movement benchmarks are dependent on the user 
applications. The Benchmarks used for the characterization are 
listed in Table 1.  
These applications ran on a 2.983 GHz Intel Core 2 CPU. We 
have used two independent mechanisms to capture the updates to 
the screen contents: an external FPGA and a Software (SW) 
mechanism and the setup of these entities are shown in Figure 1. 
The FPGA hardware intercepts the computer video output and 
captures the frames at the rate of transmission, and compares the 
current frame with the previous frame stored in the local memory 
in FPGA system. The FPGA hardware counts the pixel changes 
between the two frames at the different granularity such as the 
pixel level, macro-block level or scan line level. 
The SW mechanism leverages the existing infrastructure available 
called Damage Extension on X Window environment to track the 
changes on the screen. We modified the Linux kernel Direct 
Rendering Manager and Video driver to track the screen updates. 
The Software mechanism also produces the outputs of screen 
changes at the granularity of pixel, macro-block and scan line as 
explained below.  
A scan line is one line in the frame and usually the horizontal line 
in display. If the same scan line is different between current and 
previous frame then it is called motion scan line (MSL). 
Macroblock (MB) represents a tile of 16-by-16 pixels [7] and if 
the same MB is different between current and previous frame is 
called motion MB (MMB). Pixel, which is different between 
current and previous frame at the same location, is called motion 
pixel (MP). 
The percentage of MPs in frames gives the best indication of 
motion activity in the current frame, followed by MMB over 
MSL.  
3.2. Analysis 
This section shows three different analyses: motion frame, motion 
timing and comparison of data between HW and SW 
environments. Motion frame analysis answers to the questions of 
how many frames are really different and how much of a region is 
different between frames. Motion timing data shows how often 
the updates happen and comparison of data between the setups 
proves the validity of the methods used to obtain the data. For 
motion frame and motion timing analysis, HW data is used for 
analysis due to benchmark OS dependencies. 
3.2.1. Motion Frame Analysis 
 Figure 2 shows the motion frames on the benchmarks. 
MobileMark 2007 has low motion frame rate since this 
benchmark has lots of display idle period. 3D animation video 
playback benchmarks shows motion frames differ based on the 
decoder SW. Even though the video source decoded at 24 frame-
per-second rate, some decoder SW output shows higher frame rate 
than original source.  
 
Table 1. Benchmark frame samples. 
Benchmarks Total Frame Samping 
Duration 
Mobile Mark 2007 434669 7244 
SysMark 3D 39588 660 
SysMark E-Learning 54674 911 
SysMark Productivity 33316 555 
SysMark Video Creation 76800 1280 
3D animation (QuickTime) 39367 656 
3D animation (VLC Player) 39302 656 
Random Mouse Moves 2879 48 
Web browsing suite 5950 99 
 
 
Figure 2. Motion frames percentage in benchmarks. 
The Random Mouse Move (RMM) has the high number of 
motion frame on average since mouse cursor always moves in this 
benchmark.  
Motion frame analysis in isolation only provides one dimension of 
the frame updates information as the percentage of change within 
these motion frames will also be needed to understand the impact 
of these updates on display data traffic. From Figure 3 it is seen 
that for all the benchmarks executed there is less than 50% change 
in motion frames. Standard benchmark programs (MobileMark 
2007 and SysMark) show less than 20% of MSL, less than 8% of 
MMB and 3% of MP in motion frames. For the 3D animation 
playback, 42% of MSL, 33% of MMB, and 20% of MP are 
changed in the motion frames and these percentages could change 
depending on configuration such full screen mode. Though RMM 
benchmark shows the most motion frame updates amongst all the 
benchmarks, the actual percentage of change within these motion 
frames is minor due to the small cursor size. 
Average of motion details over entire frame rather than in motion 
frames is a meaningful indicator of the significance of updates on 
the workload.  Table 2 shows the percentage of motion in entire 
frames which include motion frames and non-motion frames. The 
Random Mouse Move benchmark shows the low percentage of 
motion over the entire frame due to small update area. The 3D 
animation video playback show an average of 10% of MP from 
entire frames, while the rest of the benchmarks show less than 1% 
of MP in entire frames. 
 
 
P-91 / K. Han
SID 11 DIGEST  •  1445
Table 2. Percentage of motion in entire frames (ML: Motion 
Scanline, MMB:Motion Macroblock, MP:Motion Pixel). 
Benchmarks MSL (%) MMB (%) MP(%) 
Mobile Mark 2007 0.52 0.18 0.08 
SysMark 3D 1.05 0.08 0.04 
SysMark E-Learning 1.24 0.28 0.17 
SysMark Productivity 4.15 1.81 0.84 
SysMark Video Creation 1.90 0.74 0.53 
3D animation (QuickTime) 26.93 21.89 12.73 
3D animation (VLC Player) 16.79 13.58 8.62 
Random Mouse Moves 3.85 0.21 0.06 
Web browsing suite 1.40 0.60 0.30 
 
 
Figure 3. Percentage of motions in motion frames (MSL: 
Motion scan line, MMB: Motion Macro-Block, MP: Motion 
Pixel). 
3.2.2. Motion Timing Analysis 
Time between motion frames is another important factor in 
display characterization. Even though some content creates 
motions, the distribution of the time between motion frames can 
be different. Figure 4 shows the distribution of the time between 
motion frames.  In 3D animation video playback benchmark, the 
time between motion frames is less than 3 frames. Mobile Mark 
2007 (MM07) test shows the bi-modal distribution in the 
histogram since the MM07 benchmark program has long pause 
periods with only clock changes. 
Mean and variance of the time between motion frames are 
parameters used to characterize the distribution. Table 3 shows 
average and variance values of the time between motion frame 
and consecutive motion frames on the workloads. The average of 
the time between motion frames are varying from 0.022 to 0.216 
second which corresponds about 1 to 13 frames. This implies that 
1 to 13 frames are consecutively identical on average. New 
display architecture can use previous frames for next frame during 
this period, thereby saving power in platform.   
Figure 5 shows the histogram of the consecutive motion frame in 
some benchmarks. From Table 3, the average of the consecutive 
motion frames vary from 0.021 to 0.799 second which 
corresponds to around 1 to 48 frames of consecutive changes. 
Table 3. Statistics of the time between motion frames and 
consecutive motion frames. 
Benchmarks Time between 
motion frames (sec) 
Consecutive motion 
frames (sec) 
Average Variance Average Variance 
Mobile Mark 2007 0.216 16.980 0.021 0.039 
SysMark 3D 0.064 49.330 0.020 0.008 
SysMark E-Learning 0.071 15.309 0.024 0.025 
SysMark Productivity 0.103 16.064 0.032 0.238 
SysMark Video 
Creation 
0.097 7.644 0.024 0.027 
3D animation 
(QuickTime) 
0.022 0.052 0.041 0.035 
3D animation (VLC 
Player) 
0.041 0.357 0.028 0.004 
Random Mouse 
Moves 
0.049 0.197 0.799 121.194 
Web browsing suite 0.151 29.674 0.049 583.059 
 
 
Figure 4. Histogram of the time between motion frames in (a) 
random mouse move, (b) MobileMark2007, (c) SysMark 
Productivity, (d) 3D Animation with Quicktime Player (Unit: 
millisecond). 
In the RMM case, the time of consecutive motion frames is very 
high compared to other benchmarks since the mouse continuously 
moved. In other benchmarks, consecutive motion time takes a 
couple of frames.   
3.2.3. Hardware and Software Data Analysis 
We have developed an independent mechanism using SW to track 
the screen updates and to ensure the validity of the data collected 
by both HW and SW setups, we executed a web browsing 
workload on both the setups simultaneously. The motion frame 
and timing data collected using the setups are shown in Table 4. 
The web browsing workload is a custom generated web script 
loading 14 web pages simulating the experience of web browsing. 
SW setup shows motion frames of 22.6% and HW data was 
measured to be 20.7% showing a difference of less than 3% 
between the setups indicating the validity of the data and accuracy 
of the models. The variation between the software and hardware 
P-91 / K. Han
1446  •  SID 11 DIGEST
  
Table 4. Statistics comparison between HW and SW characterization environment on web browsing suite. 
Characterization 
Method 
Motion Frames (%) Idle Frames (%) In Motion Frames In Entire Frames 
MSL% MMB % MP% MSL% MMB% MP% 
SW Based 22.6 77.4 20.6 14.6 13.5 4.7 3.1 3.3 
HW Based 20.7 79.3 6.8 2.8 1.4 1.4 0.6 0.3 
Difference 1.9 1.9 13.7 11.8 12.1 3.2 2.5 3.0 
 
 
Figure 5. Histogram of consecutive motion frames in (a) 
random mouse move, (b) MobileMark2007, (c) SysMark 
Productivity, (d) 3D Animation with Quicktime Player (Unit: 
millisecond). 
data for the motion frame analysis could be due to approximations 
in the SW to create an encompassing rectangle of all updates in a 
frame while the hardware accurately computes only the exact 
changes within these updates.    
4. Discussion 
The results show that only a small fraction of the screen is 
modified under most scenarios and though the distribution of 
motion frames differs based on the workload, there could be 
meaningful idleness between the motion frames.   
Display subsystem architecture can be redesigned to leverage the 
redundancy shown in the analysis. Table 2 shows that for active 
web browsing scenarios have less than 1% pixel change on 
average. A new architecture which transports only the updated 
data to panel can reduce the display related activities and save 
energy in the platform [8]. 
When the display sub-system is designed to transport to only 
frame updates, it could result in performance and power benefit in 
the sub-system. First of all, display data sent to the panel is 
reduced by a big factor and could also result in power 
consumption on the DTC and display link. As the DTC will also 
have less number of accesses to FB memory, the memory bound 
workload could have improved performance. 
It is challenging to find the updates before the data are sent to the 
panel. Han [9] proposed a detection method to detect changes by 
using checksum. Software can be one of the methods to detect the 
changes as it is more flexible to configure for the requirements.  
Even though it shows more percentages of changes within a 
motion frame, the software detection can be further optimized to 
detect the exact changes rather than an encompassing rectangle.   
5. Conclusion 
Using the FPGA and Software, motion updates are analyzed in a 
computer display system. Results show that a display system 
transports lots of duplicated data to the panel.  Some applications 
have less than 1% of motion pixels. The 99% pixel data is same as 
the previous frame. This result supports the research to develop 
new display system architecture to efficiently transport display 
frames to the panel in order to achieve low power and improve 
performance in computer displays.  
 
6. References 
[1] A. Bhowmik and R. Brennan, “System-level display power 
reduction technologies for portable computing and commu-
nications devices,” Proc. IEEE International Conference on 
Portable Information Devices, 1–5 (2007). 
[2] I. Choi, H. Shim, and N. Chang, “Low-power color TFT 
LCD display for hand-held embedded systems,” Proc. Inter-
national Symposium on Low Power Electronics and Design, 
112–117 (2002). 
[3] H. Shim, N. Chang, and M. Pedram, “A compressed frame 
buffer to reduce display power consumption in mobile sys-
tems,” Proc. of the ASP-DAC, 819–824 (2004). 
[4] P. Ranganathan, E. Geelhoed, M. Manahan, and K. Nicholas, 
“Energy-aware user interfaces and energy-adaptive dis-
plays,” IEEE Computer 39, 31–38(2006). 
[5] MobileMark 2007,WWW Document,(http://www.bapco-
.com/products/mobilemark2007) 
[6] SysMark 2007. WWW Document, (http://www.bapco-
.com/products/sysmark2007preview/) 
[7] I. Richardson, H.264 and MPEG-4 Video Compression. 
(John Wiley and Sons, 2004). 
[8] L. Hollevoet, A. Dewilde, K. Denolf, F. Catthoor, and F. 
Louagie, “A power optimized display memory organization 
for handheld user terminals,” Design, Automation and Test 
in Europe Conference and Exhibition, 3, 294 – 299 (2004).  
[9] K. Han, Z. Fang, P. Diefenbaugh, R. Forand, R. Iyer, and D. 
Newell, “Using checksum to reduce power consumption of 
display systems for low-motion content,” Proc. IEEE Inter-
national Conference on Computer Design, 47–53 (2009). 
 
P-91 / K. Han
SID 11 DIGEST  •  1447


P-91: Parametric Evaluation of Computer Displays for Optimized Display Data Transmission

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P-91: Parametric Evaluation of Computer Displays for Optimized Display Data Transmission

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