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, GuangtaoZhai, Xiaokang Yang, and WenjunZhang

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

  • SVSP in ROI scalable video coding

  • Conclusion


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.


SVSP

Feature extraction

down-sampling filter

Center surround receptive field simulation

Cross level addition and normalize

Non-linear feature combination


SVSP (1)

  • Low-level Feature Detection

    • Intensity channel :

    • Color channels :

    • Orientation channel :

    • motion channel :

Gabor filter

optical flow


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)

    • Cross level addition and normalize

    • Non-linear Feature Combination


    SVSP

    Skin & caption detection

    Post-processing

    Down-sampling filter

    SVSP integration


    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 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

    • 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-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-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

    • 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 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

    SVSP

    Filter out isolated

    Most saliency point

    Sensitive region


    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 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

    • 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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