Mpeg4 fine grained scalable multi resolution layered video encoding
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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

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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.

  • 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

  • Introduction

    • MPEG-4 Fine Grained Scalability

    • Motivation

  • FGS-AQ vs. FGS-MR

  • Experimental Results

  • Conclusion


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


FGS Encoding Block Diagram


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


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)

  • 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)

  • 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

  • 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)

  • 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


Original (5.12Mbps)


MR-Mask


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


Original (5.12Mbps)


FGS-AQ (0.17Mbps, PSNR = 22.77dB)


FGS-MR (0.17Mbps, PSNR = 26.5dB)


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


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

  • 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

  • Vary σI from 3 to 25 to adjust the target bit rate


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


Power Consumption Comparison


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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