Qian chen guangtao zhai xiaokang yang and wenjun zhang iscas 2008
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Qian Chen, Guangtao Zhai , Xiaokang Yang, and Wenjun Zhang ISCAS,2008. Application of Scalable Visual Sensitivity Profile in Image and Video Coding. Outline. Introduction Scalable visual sensitivity profile (SVSP) SVSP in noise-shaping SVSP in ROI coding of JPEG2000

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Application of Scalable Visual Sensitivity Profile in Image and Video Coding

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Qian chen guangtao zhai xiaokang yang and wenjun zhang iscas 2008

Qian Chen, GuangtaoZhai, Xiaokang Yang, and WenjunZhang

ISCAS,2008

Application of Scalable Visual Sensitivity Profile in Image and Video Coding


Outline

Outline

  • Introduction

  • Scalable visual sensitivity profile (SVSP)

  • SVSP in noise-shaping

  • SVSP in ROI coding of JPEG2000

  • SVSP in ROI scalable video coding

  • Conclusion


Introduction

Introduction

  • Computational visual attention models have been developed over the last 20 years and have already facilitated various aspects of the evolution in visual communication systems.

  • Its important applications is to enhance the image and video compression algorithms perceptually.


Application of scalable visual sensitivity profile in image and video coding

SVSP

Feature extraction

down-sampling filter

Center surround receptive field simulation

Cross level addition and normalize

Non-linear feature combination


Svsp 1

SVSP (1)

  • Low-level Feature Detection

    • Intensity channel :

    • Color channels :

    • Orientation channel :

    • motion channel :

Gabor filter

optical flow


Svsp 2

SVSP (2)

  • By iteratively down-sampling for L times of these channels

    ,we can create pyramids for each of these channels of the framei

  • Center-surround Receptive Field Simulation

  • c ∈ [0, 8], s = c + δ,

    δ ∈ [−3,−2,−1, 1, 2, 3] and s is thrown away if s ∈ [0, 8].


    Svsp 3

    SVSP(3)

    • Cross level addition and normalize

    • Non-linear Feature Combination


    Application of scalable visual sensitivity profile in image and video coding

    SVSP

    Skin & caption detection

    Post-processing

    Down-sampling filter

    SVSP integration


    Svsp 4

    SVSP(4)

    • Skin Color Detection

      • The skin color area indicates the appearance of people and often attracts human attention.

      • Hsu’s [5] skin model

    • Caption Detection

      • Luo’s[6]


    Svsp 5

    SVSP(5)

    • SVSP integration

      • Considering the fact that human face by its nature attracts more low-level human attention, we emphasize skin map more and α = 1.5, β = 1.2

    Ref.G. T. Zhai, Q. Chen, X. K. Yang, W. J. Zhang,”Scalable Visual Significance Profile Estimation”, submitted to International Conference on Acoustics, Speech, and Signal Processing, April, 2008, Las Vegas, US.


    Noise shaping

    Noise-shaping

    • To validate the effectiveness of the proposed model.

      • JND (Just-noticeable distortion/difference) :refers to the visibility threshold below which changes cannot be perceived by human.

      • Noise shaping is a popular way to evaluate the correctness of JND models.


    Noise shaping1

    Noise-shaping

    • Noise-injection process is :

    • The proposed VSP-based JND model is :

    • We will compare it with Chou’s JND model [8] JNDC and the JND model we previously proposed [9] JNDY


    Noise shaping2

    Noise-shaping

    (a)Luminance of frame 51 in president debate.

    (b)Chou’s JND model, PNSR=25.99 dB.

    (c)Yang’s JND model, PNSR=25.99 dB.

    (d)proposed VSP-based JND model, PNSR=25.99 dB.


    Roi coding of jpeg2000

    ROI coding of JPEG2000

    • We define the arbitrary ROIa in an image as areas that take half the top values in .

    • To generate a rectangular ROIr, we explore a seeded region growing algorithm , seed is placed at the most saliency point in and then expands to surroundings. The stopping criterion is that the pixel value on region borders falls below 60% of the starting seed-value.


    Roi coding of jpeg20001

    ROI coding of JPEG2000

    (a) Details of the most sensitive

    of frame 51 in president debate.

    (b) Details of image coded at 0.1bpp with

    arbitrary ROI defined in VSP, PSNR-Y=27.2dB.

    (c) Details of image coded at 0.1bpp with rectangular-shaped ROI defined in SVP, PSNR-Y=32.6dB.

    (d)Details of image coded at 0.1bpp without ROI, PSNR-Y=24.0dB.


    Roi scalable video coding

    ROI scalable video coding

    SVSP

    Filter out isolated

    Most saliency point

    Sensitive region


    Roi scalable video coding1

    ROI scalable video coding

    (a) Average PSNR-Y vs. bit rate of president debate.

    (b) Average PSNR-Y vs. bit rate of foreman.

    (c) Average PSNR-Y vs. bit rate of crew.

    (d)Average PSNR-Y vs. bit rate of coastguard.


    Roi scalable video coding2

    ROI scalable video coding

    • Visual comparison in saliency area of frame 60 in president debate, CIF size coded at 900 kbps.

    (b)with SVSP defined ROI

    (a)without ROI


    Conclusion

    Conclusion

    • This paper applies the proposed computational model for scalable visual sensitivity profile (SVSP) to image/video processing.

    • Extensive experimental results have justified the effectiveness of the proposed SVSP model.


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