1. A Fast Video Noise Reduction Method by Using Object-Based Temporal Filtering Thou-Ho (Chao-Ho) Chen, Zhi-Hong Lin, Chin-Hsing Chen and Cheng-Liang Kao Intelligent Information Hiding and Multimedia Signal Processing, 2007. (IIHMSP 2007.)

2. Outline Introduction CCD-Based Camera Noise Characteristics The Proposed Fast Video Noise Reduction Method Experimental Results Conclusions

3. Introduction Shortcomings of motion compensated filter: – Complex computation – Motion estimation accuracy decreases greatly when videos are corrupted by high level noise. – Blocking artifacts Introduction of current non-motion compensated filters:

4. Introduction 1. The video alpha-trimmed mean filter[5]: – Consider a 3 x 3 x 3 window around (z, y, t): Pros: reduce video noise effectively. Cons: cause edge-jittering. [5] V. Zlokolica, W. Philips and D. Van De Ville, “Robust non-linear filtering for video processing”, 14 th International Conference on Digital Signal Processing, DSP 2002., Vol. 2, Pages: 571 - 574, July 1-3, 2002. where θ = N – 2 *[α*N ]

5. Introduction 2. Simple frame averaging technique [7]: Pros: good at Gaussian noise reduction. Cons: useless at impulse noise reduction. N: number of averaged frames (n 1, n 2 ): position [7] J. M. Boyce, “Noise reduction of image sequences using adaptive motion compensated frame averaging” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 3, Pages: 461 - 464, 23-26 March. 1992.

6. Introduction 3. Switching median (SM) filter [2]: – Output: large r ij => isolated impulse [2] Shuqun Zhang and M.A. Karim, “A new impulse detector for switching median filters”, IEEE Signal Processing Letters, Vol. 9, Issue 11, Pages: 360 - 363, Nov. 2002.

7. Introduction 3. Switching median (SM) filter [2]: Pros: good at impulse noise reduction. Cons: useless at Gaussian noise reduction. [2] Shuqun Zhang and M.A. Karim, “A new impulse detector for switching median filters”, IEEE Signal Processing Letters, Vol. 9, Issue 11, Pages: 360 - 363, Nov. 2002.

8. Introduction 4. Interframe temporal comparison [11]: – M: arbitrary square window ; N: the whole frame Using special comparison methods to removal the dim noise and bright noise. [11] Chen Qian, Bai Lianfa and Zhang Baomin, “Real-time adaptive noise processing in low light level images”, IEEE Transactions on Signal Processing, Vol. 1, Pages: 606 – 609, 14-18 Oct. 1996.

9. CCD-Based Camera Noise Characteristics In traditional CCD-based cameras use the Bayer pattern[R(25%), G(50%) and B(25%)]. The False Color Noise (FCN) cause by parallel ADC generate different clipping points. The energy of FCN is distributed over each color component.

10. CCD-Based Camera Noise Characteristics The spatial filtering cannot effectively remove FCN and the temporal filtering is necessary. Real noise arises from the environmental luminance is insufficient.

11. The Proposed Fast Video Noise Reduction Method

12. Compute luminance average of the frame: – where w and h is the videos width and height Divided all range of luminance into three parts: 0 ≤ Y ≤ T 2 ;T 2 < Y ≤ T 1 ;T 1 < Y ≤ 255 According to different CCD-based cameras: – T 1 is around 125 ~ 135 – T 2 is around 75 ~ 85

13. The Proposed Fast Video Noise Reduction Method Then, compute the difference: Calculate the histogram of these two difference and find the maximum values: hist1_MAX & hist2_MAX hist1_MAX Histogram of abs_difference_1

14. The Proposed Fast Video Noise Reduction Method Define “noise_margin” by stationary region at each video segment (stationary or dynamic): The initialize “noise_margin” under different light level image: – high light level: 9 ~ 14 – median light level: 13 ~ 18 – low light level: 23 ~ 28

15. The Proposed Fast Video Noise Reduction Method Frame will be judged into a stationary if: Frame will be judged into a dynamic if: Under high and median light level, T = 4 Under low light level, T = 3.

16. The Proposed Fast Video Noise Reduction Method

17. If current frame is dynamic, to differentiate between stationary and moving regions: NR for stationary regions(frame): NR for moving regions: moving stationary

18. Experimental Results The speed improvement rate (SIR): The compression improvement rate (CIR): – OS: size of original video – PS: size of compressed video Filter-1: alpha trimmed mean filter [5] Filter-2: SM filter [2] Filter-3: simple temporal filter [6] Filter-4: inter frame temporal comparison filter [11]

19. Experimental Results

20. Experimental Results

21. Conclusions A novel fast inter frame based temporal filter is proposed. The proposed filter can substantially reduce video noise while restoring details. In terms of filtering speed and visual quality, the proposed filter will be more attractive under a moderate compression ratio.