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Video Quality Assessment via Spatiotemporal Saliency Weighted SSIM

Video Quality Assessment via Spatiotemporal Saliency Weighted SSIM Yilin Wang, Qiang Zhang, Baoxin Li. Outline. Motivation and Related Work Proposed Method and Evaluation Conclusion. Outline. Motivation and Related Work Motivation Background Our objective Related work

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Video Quality Assessment via Spatiotemporal Saliency Weighted SSIM

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  1. Video Quality Assessment via Spatiotemporal Saliency Weighted SSIM Yilin Wang, Qiang Zhang, Baoxin Li

  2. Outline • Motivation and Related Work • Proposed Method and Evaluation • Conclusion

  3. Outline • Motivation and Related Work • Motivation • Background • Our objective • Related work • Proposed Method and Evaluation • Conclusion

  4. Motivation • Online video sharing services. • Video conference. • Video compression… Information explosion and overload problem in visual data. Subjective evaluation is prohibitively expensive and time consuming.

  5. Background Query from YouTube Any efficient and human perceptual method? Apply PSNR and SSIM frame by frame ? Likely to pay more attention to interest region.

  6. Our Goal Extract spatiotemporal interest region efficiently. Video retargeting. Video summarization. Object detection.

  7. Our Objective Spatiotemporal Saliency SSIM

  8. OurObjective SSIM

  9. Related Work • Wang et al. IW-SSIM. 2011 • You et al. Attention model, 2011 • ….

  10. Outline • Motivation and Related Work • Proposed Method and Evaluation • Spatiotemporal saliency model • Incorporating with SSIM • Dataset and evaluation • Conclusion

  11. Spatiotemporal Saliency • Input : 3D data • Output: 3D saliency map • Time complexity

  12. Spatiotemporal Saliency • =

  13. Analysis Why ? Hou et al find it is the phase information rather than spectrum that leads a saliency map. (other than QFT for video) Condition: Background is sparsely supported in the DCT domain and the foreground is sparsely support in the spatial domain. QFT HFT FFT

  14. Incorporation with SSIM Saliency pooling strategy : Perceptual video quality index:

  15. Experiment • Saliency Evaluation: • Benchmark dataset: CRCNS-ORIG • 50 video clips from different genres. • Ground truth: eye fixation data from 8 subjects. • Evaluation method: we compute the area under the curve with state of the arts method.

  16. Saliency Evaluation

  17. Video Quality Evaluation • Dataset : LIVE VQA dataset. • Evaluation: PLCC , SROCC, RMSE • The logistic regression function recommended by VQEG:

  18. Video Quality Evaluation • Saliency models are incorporated with SSIM, PSNR.

  19. Video Quality Evaluation

  20. Outline • Motivation and Related Work • Proposed Method and Evaluation • Conclusion

  21. Conclusion

  22. Conclusion • Our goal : efficient & effective VQA algorithms. • Time Complex VS Performance is difficult to balance. • More sophisticated pooling algorithm should be employed. (You 2011 ACM MM)

  23. Thank You

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