Energy Consumption Analysis of Image Encoding and Decoding Algorithms

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Energy Consumption Analysisof Image Encoding and Decoding

Algorithms Mohammad Rashid, Luca Ardito, and Marco Torchiano

GREENS 2015

MotivationEnergy consumption represents an

important issue with limited and embedded devices

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Plan

Object: image codec algorithms

Purpose: assessing differences

Focus: energy consumption

Context: Raspberry PI

Research Questions

• RQ1: Do different codecs consume different amounts of energy for encoding/decoding images?

• Metric: total energy

• RQ2: How closely are energy consumption and computational performance correlated?

• Metrics: Enegy vs. time, and Power

Test ImagesA

B

C

768K

2.4M

3.2M

CodecsPNG JPG, q:10

JPG, q:80JPG, q:40

Experimental design

.BMP

PNG Encoder

JPG Encoder

JPG Encoder

JPG Encoder

Q10

Q40

Q80

PNG Decoder

JPG Decoder

JPG Decoder

JPG Decoder

InstrumentationRaspberry Pi + USB Tester OLED Backpack 2.0

RQ1: codec consumption

Task ☞ DECODING ENCODING

CODEC☟ Energy Time Energy Time

PNG 1.78 9.6 6.16 39.0

JPEG, Q:10 0.94 4.5 1.05 7.6

JPEG, Q:40 0.76 3.4 0.84 2.7

JPEG, Q:80 0.68 2.9 0.78 2.7

Average values over the three images, values for five repetitions of the task

Consumption

==

70k PNG decode

130k JPG:10 decode

160k JPG:40 decode

180k JPG:80 decode{ or

or

or

(c) iFixIt - Walter Galan

RQ2: Energy and complexity

0.0

2.5

5.0

7.5

10.0

0 20 40Time [s]

Ener

gy [s

]

ProcessDecodeEncode

E = P · t

Mean Power vs. Total Energy

A

B

C

A

B

C

Decode

Encode

jpg:Q80 jpg:Q40 jpg:Q10 png:LLCodec:Quality

Image

0.15

0.17

0.20

0.23

Power A

B

C

A

B

C

Decode

Encode

jpg:Q80 jpg:Q40 jpg:Q10 png:LLCodec:Quality

Image

2.5

5.0

7.5

Energy

Key Findings

• Decoding a PNG image consumes ~2 times as much energy as for a JPG image • ~6 times for encoding

• Energy consumption is strongly correlated to time • Other factors affect the energy marginally

• The average power consumption variation among algorithms is ~10%

Open Questions

• Can we identify algorithms that with a similar computational complexity consume less power?

• What are the features in software that can affect power consumption?