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MPEG4 Fine Grained Scalable Multi-Resolution Layered Video Encoding. Authors from: University of Georgia Speaker: Chang-Kuan Lin. Reference. S. Chattopadhyay, S. M. Bhandarkar, K. Li, “ FGS-MR: MPEG4 Fine Grained Scalable Multi-Resolution Layered Video Encoding ,” ACM NOSSDAV 2006 .

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mpeg4 fine grained scalable multi resolution layered video encoding

MPEG4 Fine Grained Scalable Multi-Resolution Layered Video Encoding

Authors from: University of Georgia

Speaker: Chang-Kuan Lin

reference
Reference
  • S. Chattopadhyay, S. M. Bhandarkar, K. Li, “FGS-MR: MPEG4 Fine Grained Scalable Multi-Resolution Layered Video Encoding,” ACM NOSSDAV 2006.
  • W. Li, “Overview of Fine Granularity Scalability in MPEG-4 Video Standard,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 11, No. 3, pp. 301-317, Mar. 2001.
  • H. Radha, M. van der Schaar, and Y. Chen, “The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP,” IEEE Trans. on Multimedia, vol.3, pp. 53–68, Mar. 2001.
outline
Outline
  • Introduction
    • MPEG-4 Fine Grained Scalability
    • Motivation
  • FGS-AQ vs. FGS-MR
  • Experimental Results
  • Conclusion
introduction
Introduction
  • MPEG4 Fine Grained Scalability (FGS) profile for streaming video
    • Base Layer Bit Stream
      • must exist at the decoder
      • has coarsely quantized DCT coefficients
      • provides the minimum video quality
    • Enhancement Layer Bit Stream
      • can be absent at the decoder
      • contains encoded DCT coefficient differences
      • provides higher quality
      • can be truncated to fit the target bit rate
motivation
Motivation
  • Base Layer video quality is usually not satisfactory
    • in order to provide a wide range of bit rate adaptation
  • MPEG4 FGS Adaptive Quantization (FGS-AQ) for Base Layer video does not provide good rate-distortion (R-D) performance
    • parameter overhead at the decoder
  • Proposed FGS-MR
    • no parameter overhead to transmit
    • transparent the codec
    • better rate-distortion performance
outline1
Outline
  • Introduction
    • MPEG-4 Fine Grained Scalability
    • Motivation
  • FGS-AQ vs. FGS-MR
    • FGS-AQ
    • FGS-MR
      • MR-Mask Creation
      • MR-Frame
  • Experimental Results
  • Conclusion
fgs adaptive quantization aq
FGS Adaptive Quantization (AQ)
  • Goals
    • To improve visual quality
    • To better utilize the available bandwidth
  • Method
    • Define different quantization step sizes for different transform coefficients
      • within a macro-block(low freq. DCT coeff. => small step size)
      • for different macro-blocks(different quantization factors)
  • Disadvantages
    • R-D performance degrades due to FGS-AQ parameter overhead
proposed multi resolution fgs fgs mr
Proposed Multi-Resolution FGS (FGS-MR)
  • Goal
    • To improve the visual quality
    • To better utilize the available bandwidth
    • No transmission overhead and hence maintaining the R-D performance
  • Method
    • Apply a low-pass filter on “visually unimportant” portion of the original video frame before encoding.
two equivalent operations
Two Equivalent Operations
  • Apply a low-pass filter on the spatial domain of an image
  • Truncate DCT coefficients in the corresponding transform domain of an image
fgs mr process step 1
FGS-MR Process (Step 1)
  • MR-Mask creation
    • Use Canny edge detector to detect edges
    • Weight Mask
      • an weight parameter wi, j for each pixel p(i, j) of an image, 0 ≦ wi, j ≦1
      • wi, j = 1, if p(i, j) is on the edge
      • 0 < wi, j ≦1, if p(i, j) is near edge
      • wi, j = 0, if p(i, j) is in non-edge region
fgs mr process step 2
FGS-MR Process (Step 2)
  • MR-Frame Creation
    • VI = (I-W) VL +W VH
    • VF = Iteration( VI, G(σI))
  • Note
    • VI contains abrupt changes in resolution
    • VF is a smooth version of VI

Parameters

  • Vo: original video frame
  • VL: low resolution frame from the convolution of Vo and G(σL)
  • VH: high resolution frame from the convolution of Vo and G(σH)
  • VI : intermediate video frame
  • VF : final multi-resolution frame
  • I: matrix with all entries as 1
  • W: MR-mask weight matrix
  • G(σ): Gaussian filter with standard deviation of σas LPF
  • σL >σH
determine parameters
Determine Parameters
  • σL, σH, and σI
    • to control the bit rate
  • W (weight matrix)
    • to control the quality of the encoded video frame
  • Figure of merit function: δ=Q/C
    • Q = 2^( PSNR(σL, σH, σI)/10 )
    • or PSNR = 10log(Q)
    • C: compression ratio
  • The authors empirically determine the parameters
    • σL = 15, σL = 3, and varying σI
outline2
Outline
  • Introduction
    • MPEG-4 Fine Grained Scalability
    • Motivation
  • FGS-AQ vs. FGS-MR
    • FGS-AQ
    • FGS-MR
  • Experimental Results
    • Rate Distortion
    • Resource Consumption
  • Conclusion
experiments
Experiments
  • Video 1
    • 320x240, fps = 30
    • A single person walking in a well lighted room
  • Video 2
    • 176x144, fps = 30
    • A panning view across a poorly lighted room.
    • No moving object
rate distortion performance
Rate Distortion Performance
  • Vary σI from 3 to 25 to adjust the target bit rate
power consumption
Power Consumption
  • Energy used and hence power consumed by wireless network interface card (WNIC):

T: time duration

S: data size

b: the bit rate of streaming video

B: available BW

ER: energy used by WNIC during data reception

Es: energy used by WNIC when sleeping

conclusion
Conclusion
  • The rate distortion performance of FGS-MR is better than FGS-AQ.
  • FGS-MR can be seamlessly integrated into existing MPEG4 codec.
  • My comment
    • Processing time issue of FGS-MR
    • Empirical determined filter parameters
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