No-Reference Metrics For Video Streaming Applications - PowerPoint PPT Presentation

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No-Reference Metrics For Video Streaming Applications

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Title: No-Reference Metrics For Video Streaming Applications


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
  • Whats Quality ?
  • - Implies Comparison gt reference
  • Three Techniques
  • - Full-Reference eg. MSE, PSNR
  • - Reduced Reference
  • - No Reference

3
What is a No-Reference metric ?
  • Estimating end-users 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.
  • Whats blockiness ?
  • Distortion of image characterized by
    appearance of underlying block encoding structure

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8
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 s
  • - block-edge gradient ?

9
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 skl
  • calculate ?kl

10
E1
I1
An 8 x 8 block and its edges
E2
I2
Bij
I4
E4
I3
E3
a3
a2
a1
7
Three segments akl of a
block edge
11
NR Blockiness Metric contd
  • CB No. of Blocks for which at least one edge
    satisfies
  • skl lt e where e 0.1
  • ?kl gt t where t 2.0
  • e min. spatial activity required to mask
    gradient
  • t max. gradient which is imperceivable.
  • ßF CB / Total no. of blocks in the frame

12
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 gt compression level

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16
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

17
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

18
Macroblock 1
Macroblock 2
Figure Calculating Strength vector across and
within a macroblock
19
  • Convert strength vectors into binary vectors
  • Ej(k) 1 if Êj gt t
  • 0 otherwise
  • E?j(k) 1 if Ê?j gt t
  • 0 otherwise
  • where t 15

20
  • 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) gt ?
  • 0 otherwise
  • where ? 10 of frame width (n)
  • Packet loss metric for whole frame
  • F ? Hj2

21
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 .

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24
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.

25
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

26
References
  • No Reference Image and Video Quality Assessment
    http//live.ece.utexas.edu/research/quality/nrqa.h
    tm
  • 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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