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Application of Scalable Visual Sensitivity Profile in Image and Video Coding

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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  1. Qian Chen, GuangtaoZhai, Xiaokang Yang, and WenjunZhang ISCAS,2008 Application of Scalable Visual Sensitivity Profile in Image and Video Coding

  2. Outline • Introduction • Scalable visual sensitivity profile (SVSP) • SVSP in noise-shaping • SVSP in ROI coding of JPEG2000 • SVSP in ROI scalable video coding • Conclusion

  3. 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.

  4. SVSP Feature extraction down-sampling filter Center surround receptive field simulation Cross level addition and normalize Non-linear feature combination

  5. SVSP (1) • Low-level Feature Detection • Intensity channel : • Color channels : • Orientation channel : • motion channel : Gabor filter optical flow

  6. 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].

  7. SVSP(3) • Cross level addition and normalize • Non-linear Feature Combination

  8. SVSP Skin & caption detection Post-processing Down-sampling filter SVSP integration

  9. 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]

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. ROI scalable video coding SVSP Filter out isolated Most saliency point Sensitive region

  17. 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.

  18. 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

  19. 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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