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Feature-Cluster Driven VQA Framework for Video Quality Assessment in Digital Transmission

This paper discusses a comprehensive feature-cluster driven Video Quality Assessment (VQA) framework aimed at improving video quality during transmission in digital broadcast systems, particularly for IP and broadband services. It models subjective perceptions of video quality by integrating backend transmission analysis with end-user device assessments. The framework utilizes objective measures for video artifacts, assesses the impact of transmission errors, and employs a sophisticated modeling approach that includes a robust training and testing process using real-world data, ensuring high reliability in subjective visual perceptions.

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Feature-Cluster Driven VQA Framework for Video Quality Assessment in Digital Transmission

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  1. Feature-cluster drivenVQA Framework Vladimir Zlokolica VQEG-JEG, Hillsboro, Oregon Dec. 2011

  2. Digital Video Transmission chain Capturing Device Video Quality Assessment processing platform IP Broadband Services and Applications Set-Top Box Digital Broadcast HDMI Digital Audio-Video Content Monitoring/Control Working Station

  3. Main Concept • Modeling visual subjective perception on end-user device. • Combine both (i) video quality of the received multimedia at the back-end side of the transmission chain and (ii) the display at the end-user device. • Model the impact of the video content and video entertainment type to the true visual perception. • Modeling the influence of the video content to amount of impaired video artifacts, objectively measured, given the transmission channel conditions. • Modeling the influence of transmission errors to end-user device decoding functionality and hence visual quality. • Indicate cases the cause poor decoding performance.

  4. Black box testing for TV systems Video TV & Video Audio RC Comm. PC based BBTesting platform software

  5. Real case scenario ffmpeg reader –> PES Packetizer –> TS multiplexer Sirannon FFMPEG encoder Modulator YUV TS DVB-S/C/T AVI Hardware Attenuator TRILITHIC Demodulator ALITRONICA Live DVB-S/T/C Multi-channel TS Extract one video stream ffmpeg reader –> PES Packetizer –> TS multiplexer -> Basic Scheduler -> RTP/UDP/TCP Transmitter Back loop streaming Elementary stream Wireshark FFMPEG decoder PCAPtoTS PCAP TS YUV Generated files used for quality model evaluation

  6. Feature-cluster driven VQA framework • VQA model design phase • Generating training test streams with realistic content and impaired artifacts. • Selecting set of objective measures for artifact-free video content and video artifacts. • Classify feature samples of the objective measures to corresponding video quality models. • VQA exploitation phase • Computing video feature samples and allocating them to the corresponding video quality model based on their cluster membership.

  7. Training and test sequences used • Combined LIVE data base and SD VQEG sequences – in total 19 sequences • 13 training sequences randomly chosen • 6 test sequences randomly chosen • Different exact format • The first set has 4 different degradation levels based on variable bit-rate and the second has 5 fixed bit-rates. • Two different sequence sets with two independent MOS (ITU-R BT.500-11) obtained.

  8. Feature-cluster driven VQA framework VQA Design Phase MOS1 objective measures QM1 EMOS1 MOS MOS2 Compute objective measures (features) Feature Reduction/ Selection Feature Sample Clustering Training video sequences QM2 EMOS2 MOSL Selected K-dimensional feature samples Objective feature samples QML EMOSL

  9. List of considered objective measures • Measures for: • Contrast • Color • Brightness • Amount of motion: global and local • Spatial variance, • Homogeneity, correlation, mean, median • Spatiotemporal gradients • Blocking (intensity and occurrence), blurring, blocking • Other higher level measures such as freezing detection, packet-loss detection,…

  10. Feature reduction/selection • Optional: Feed forward selection scheme.

  11. Feature Clustering MOS MOS1 EMOS0 QM0 QM1 EMOS1 MOS2 Feature Sample Clustering QM2 EMOS2 MOSL Selected K-dimensional feature samples QML EMOSL

  12. Feature-cluster driven VQA framework VQA Exploitation Phase wL Determine cluster membership w1 w0 EMOS0 QM0 Compute objective measures (features) EMOSf EMOS1 QM1 Feature samples Test video sequences EMOSL QML

  13. Results for multiple linear models

  14. Results for multiple linear models

  15. Results for multiple non-linear models

  16. Comparison between linear and non-linear models

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