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No-Reference Metrics For Video Streaming Applications

No-Reference Metrics For Video Streaming Applications. International Packet Video Workshop (PV 2004) Presented by : Bhavana CPSC 538 February 21, 2004. Video Quality Assessment. What’s Quality ? - Implies Comparison => reference Three Techniques : - Full-Reference eg. MSE, PSNR

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No-Reference Metrics For Video Streaming Applications

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  1. No-Reference Metrics For Video Streaming Applications International Packet Video Workshop (PV 2004) Presented by : Bhavana CPSC 538 February 21, 2004

  2. Video Quality Assessment • What’s Quality ? - Implies Comparison => reference • Three Techniques : - Full-Reference eg. MSE, PSNR - Reduced Reference - No Reference

  3. What is a No-Reference metric ? • Estimating end-user’s QoE of a multimedia stream without using an original stream as a reference. • In other words : “ Quantify quality via blind distortion measurement”

  4. Purpose • To evaluate two types of distortions in streaming of compressed video over packet-switched networks - Compression related : block-edge impairment - Transmission related : packet-loss impairment

  5. Where Can It Be Used ? • For real time monitoring . • Reference unavailable or expensive to send • Feedback to Streaming Server . • Evaluation of Compression Algorithms

  6. What are Block-Based Codecs ? • Process several pixels of video together in blocks • At high compression rates, strong discontinuities called block edges come up. • What’s blockiness ? “Distortion of image characterized by appearance of underlying block encoding structure

  7. Block – based Distortion • Idea : A block-edge gradient can be masked by a region of high spatial activity around it . • Measure two things : - spatial activity around block edges : σ - block-edge gradient : Δ

  8. Calculation of NR Blockiness Metric • For each 8 x 8 Block Bij , For each edge Ik of Bij, divide edge into 3 segments akl For each segment of akl calculate σkl calculate Δkl

  9. 0 1 2 3 4 5 6 E1 I1 An 8 x 8 block and its edges E2 I2 Bij E4 I4 I3 E3 a3 a2 a1 7 Three segments akl of a block edge

  10. NR Blockiness Metric contd’ • CB = No. of Blocks for which at least one edge satisfies : σkl < εwhere ε = 0.1 Δkl > τwhere τ = 2.0 ε = min. spatial activity required to mask gradient τ= max. gradient which is imperceivable. • βF= CB / Total no. of blocks in the frame

  11. Simulation Setup For NR Blockiness Metric • Aim : to measure how well the NR Blockiness metric conveys QoE • Codec : MPEG -4 , GOP = 30 frames • Bit Rate => compression level

  12. NR Packet Loss Metric • Error Concealment : Replace damaged/lost macroblock with corresponding macroblock from previous frame. • Idea : Use length of artifact to estimate amount of distortion caused by packet loss

  13. Calculation of NR Packet Loss Metric For a m x n frame For each 16 x 16 macroblock Calculate : Êj= strength vector across macroblock edge Ê΄j= strength vector within macroblock near the edge

  14. Macroblock 1 Macroblock 2 Figure : Calculating Strength vector across and within a macroblock

  15. Convert strength vectors into binary vectors Ej(k) = 1 if Êj > τ = 0 otherwise E΄j(k) = 1 if Ê΄j > τ = 0 otherwise where τ = 15

  16. If the sum of differences between the two binary edge vectors is substantial , then there is distortion • Packet loss metric for jth macroblock Hj = ∑ | Ej(k) - E΄j(k) | if ∑ | Ej(k) - E΄j(k) | > ζ = 0 otherwise where ζ = 10% of frame width (n) • Packet loss metric for whole frame F = ∑ Hj2

  17. Simulation Setup for NR Packet Loss Metric • Bit Rate = 1.5 Mbps • Frame Rate = 30 fps • Frame Size = 352 x 240 • Used NTT DoCoMo packet loss generating software .

  18. Limitations Of NR-metrics • Blockiness metric might fail in the presence of strong de-blocking filters which might otherwise introduce blur • Metric predictions lose meaning in presence of other distortions like blur, noise etc.

  19. Future Directions • VQEG standardization efforts • HVS based approaches • Statistical models for natural scenes • NR QA schemes for - Non-block based compression schemes such Wavelet-based -Targeting full range of artifacts

  20. References • No Reference Image and Video Quality Assessmenthttp://live.ece.utexas.edu/research/quality/nrqa.htm • Objective video Quality Assessment http://www.cns.nyu.edu/~zwang/files/papers/QA_hvd_bookchapter.pdf • Perceptual Video Quality and Blockiness Metrics for Multimedia Streaming Applications www.stefan.winkler.net/Publications/wpmc2001.pdf

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