References[1] Ramanathan Palaniappan, Nitin Suresh and Nikil Jayant, “Objective measurement of transcoded video quality in mobile applications”,IEEE MoVID 2008 (workshop as a part of WoWMoM 2008), Newport Beach, CA, June 2008.

Conclusion & Current Work

• MTBF estimates from AVQ scores :– show a wide range across transcoding bit rates

and codecs.

– are subjectively more meaningful.

– better represent the slight variations in degraded

visual quality.

• Our current focus (in a Cisco Research Project) : – the analysis of network artifacts (NA) on video

quality

– Using these automatic VQ measurements to

enhance the streaming of IP video

Today’s Video Delivery Scenario

The Video Quality (VQ) Challenge

• Develop subjectively meaningful metrics

• Must be objective, enabling real time computation

• Zero Reference (ZR) nature – independent of source video

Our Solution

• Mean Time Between failures (MTBF) – Failure refers to video artifacts deemed to be

perceptually noticeable

– Directly related to Mean Opinion Score (MOS)

• Automatic Video Quality (AVQ)– Objective estimate of MTBF for the transcoding

experiments

Metrics used in our work

• PSNR – Simple full Reference metric

– Computes pixel by pixel difference per frame

basis b/w source and processed video

– Actual value not definitive but comparison b/w two

values measures quality

• Automatic Video Quality (AVQ)– Zero Reference metric

– Based on Spatio temporal algorithms and

knowledge of Human Visual System

AVQ Metric

• Computation based on output pixel values• AVQ score shows excellent subjective attributes

Our Transcoding Platform – VLC

• Flexible transcoding options like different codecs, variable bit rate, GOP size etc.

• 2 transcoding operations– MPEG 2 to H.264

– MPEG 2 to MPEG 4 SP

Experimental Setup

Results

Sample Frames

Objective Measurement of Transcoded Video Quality in Mobile Applications

Ramanathan Palaniappan, Nitin Suresh and Nikil JayantSchool of ECE, Georgia Institute of Technology, Atlanta, GA 30332

Network

Video Server & Transcoder

Projectors

3G Smart Phones

HD TVs

LaptopsLCD Displays

MPEG 2 to H.264Transcoder

384 – 192 Kbps

MPEG 2 to MPEG 4Transcoder

384 – 192 Kbps

MPEG 2 Transrater384 – 192 Kbps

MPEG 2 512 Kbps72 sec (QCIF)

H.264

MPEG 4

MPEG 2

0 500 1000 150020

30

40

50

60

70PSNR vs frame number @ 192 Kbps

Frame number

PS

NR

(dB

)

H.264MPEG 4MPEG 2

0 500 1000 15000

0.1

0.2

0.3

0.4

0.5

0.6

0.7AVQ CA score vs frame number @ 192 Kbps

Frame number

AV

Q C

A s

core

H.264MPEG 4MPEG 2

0 500 1000 15000

0.1

0.2

0.3

0.4

0.5

0.6

0.7AVQ CA score vs frame number for MPEG 4

Frame number

AV

Q C

A s

core

384 Kbps256 Kbps192 Kbps

0 500 1000 150025

30

35

40

45

50PSNR vs frame number for MPEG 4

Frame number

PS

NR

384 Kbps256 Kbps192 Kbps

Fig. 1 : (a) plots PSNR for the video transcoded into H.264, MPEG 4 and MPEG 2 at 192 Kbps. (b) plots AVQ compression artifact (CA) score for the same case (Range : 0 – best and 1 – worst).

(a) (b)

Fig. 2 : (a) plots PSNR for the same video transcoded at 192, 256 and 384 Kbps with the MPEG 4 transcoder. (b) plots AVQ compression artifact (CA) score for the same case.

(a) (b)

Bit rate 192 kbps 256 Kbps 384 Kbps

Codec MTBF PSNR MTBF PSNR MTBF PSNR

H.264 546 36.51 1406 37.31 10000 38.24

MPEG-4 7 34.37 22 35.65 1968 37.39

MPEG-2 5 33.90 13 34.88 140 36.78

Table 1 : PSNR (dB) & estimated MTBF (sec) based on AVQ CA Score

(a) (c) (b)

Fig. 3 : Frame # 508 transcoded at 192 Kbps into (a) MPEG 2, (b) MPEG 4 & (c) H.264

Fig. 4 : Frame # 1469 transcoded into MPEG 4 at (a) 192 Kbps, (b) 256 Kbps & (c) 384 Kbps

(a) (b) (c)