Modelling of Quality of Experience in No-Reference (NR) Model

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“Modelling of Quality of Experience (QoE) in No-Reference Model”Mikołaj Leszczuk, Lucjan Janowski,Jakub Nawała

25.11.2016

QoE Measurementin Full-Reference Model

PSNR SSIM VIF VQM

Diagram source: École Polytechnique Fédérale de Lausanne (EPFL), “Objective Quality Assessment”, 15.03.2011, http://mmspg.epfl.ch/page-58337-en.html

QoE Measurementin No-Reference Model

Blockiness Blur Exposure Time Noise Slicing Block Loss

Freezing (Jerkiness) Blackout Contrast Brightness Letterbox Pillarbox

Interlace Flickering Temporal Activity

Spatial Activity

Diagram source: École Polytechnique Fédérale de Lausanne (EPFL), “Objective Quality Assessment”, 15.03.2011, http://mmspg.epfl.ch/page-58337-en.html

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QoE Measurement Software Package Working in No-Reference Model

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15 Independent Quality Indicators» Blackout» Blockiness» Block Loss» Blur» Contrast» Exposure» Flickering» Freezing» Interlacing» Letter-boxing» Noise» Pillar-boxing» Slicing» Spatial Activity (SA)» Temporal Activity (TA)

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Details (1/2)» No need to access

source sequences» Transparent to video

codec:– Operating on

decompressed video frames

– Even HDMI capture

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Details (2/2)

» Utilizes modern multi-threaded architecture of processing units

» Metrics don’t use closed libraries (source code is easily portable)

» Designed modularly (modularity = easier testing and integration)

» Based on our and others’ scientific work

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No Uniform MOS ScaleMapping

» Unknown specific user» Different needs for different scenarios» Difficult to compare our work with others’» Necessity of such mapping definition in future» Partial solution is Support Vector Machine

(SVM) model mapping on Video Quality Metric (VQM)

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VQM Mapping

» All sequences: 1080p from Consumer Digital Video Library (CDVL)

» Source sequences (SRC) split to chunks 2 seconds long

» Total number of chunks: 361» 10 different compression conditions (HRC)» VQM calculated for 2 seconds sequences

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Predictors

» Blockiness» Spatial Activity (SA)» Blur» Noise

» SVM (R-Squared form 0.6 to 0.76)» Linear model (Adjusted R-Squared 0.689)

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Set 1, R2: 0.595

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Predicted

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Set 1, R2: 0.595

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Set 5, R2: 0.765

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Predicted

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Set 5, R2: 0.765

Project Web Page:http://vq.kt.agh.edu.pl/

Free ImplementationAvailable for Research Purposes

» Our target = easy availability of our work

» Software available on all 3 popular platforms

» Serving as reference implementation for algorithms developed

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Status

» Public funding finished» Looking for cooperation

with business partners to keep project going

» Known partner = known user needs = better addressing them

» Known target group = more precise results

Contacthttp://vq.kt.agh.edu.pl

vq@kt.agh.edu.pl