Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation
Abstract
:1. Introduction
Target Applications | Denoising Algorithms |
---|---|
Single view | NLM [8] |
BM3D [9,10] | |
SGN [11] | |
Real image denoising [12] | |
Universal denoising networks [13] | |
FFDNet [14] | |
Video | VBM3D [15] |
VBM5D [16] | |
With optical flow estimation [17] | |
Frame-to-frame training [18] | |
SALT [19] | |
Multi-view | NLM with PS [20] |
With occlusion handling [21] | |
MVCNN [22] | |
LFBM5D [24,25] | |
APA [26] |
2. Overview
- Searching time accounts for nearly half of the total time in BM3D-like approaches such as BM3D, VBM3D, VBM4D and LFBM5D. In this paper, both the search area and the difference calculation are changed from 2D to 1D considering the characteristics of 1D linear geometry. Thus, the searching speed is increased.
- The naïve block-matching search used in the conventional BM3D-like approaches does not work well with noise. For an efficient search in the noisy views, the proposed 1D window-based search using EPI, which accurately reflects the characteristics of multi-view, reduces the risk of incorrect patches. In addition, the maximum disparity is reflected in the ELS range of the initial views (denoted as SR1step hereinafter) as meaningful searching guidance for noisy images. Subsequently, the remaining views limit the search range (denoted by SR2step hereinafter) around the best point found in the previous views to prevent an incorrect outcome due to noise.
- Most existing denoising algorithms simply consider the SAD in block search process. However, the proposed ELS plays the role of pre-processing for the block-based ME to be carried out for the denoised multi-view compression. When searching for the correspondence in each view, the slope similarity of the linecor-pixel with adjacent pixels as well as the pixel difference is considered together to reflect the rate-distortion optimization of ME. This has a positive effect in the subsequent compression stage of denoised multi-views.
- Aggregation using weighted summation helps denoised results to be spatially consistent within the image in BM3D-based approaches. Hereinafter, this is referred to as spatial aggregation. In this paper, both spatial and temporal aggregations are adopted. The correlation between the views is increased by performing inter-view level temporal aggregation from the filtering results for the patches of each view. This contributes to increasing the compression efficiency.
3. Proposed Compression-Friendly Denoising
3.1. Fast and Noise-Resistanti EPI Estimation
3.2. Temporal Aggregation
4. Performance Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sequence | σ | PSNR (dB) | BDBR (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BM3D | VBM4D | MS-EPLL | 3D-Focus | MVCNN | LFBM5D | Proposed | VBM4D | MS-EPLL | 3D-Focus | MVCNN | LFBM5D | Proposed | ||
Champagne (1280 × 960) | 15 | 38.07 | 37.40 | 38.06 | 25.91 | 33.36 | 34.43 | 36.08 | −9.22 | +6.00 | −22.47 | −43.07 | +9.00 | −14.97 |
25 | 35.23 | 34.72 | 35.78 | 25.06 | 31.08 | 30.90 | 33.93 | −6.58 | −6.73 | −2.75 | −51.97 | +17.81 | −20.98 | |
35 | 33.33 | 32.57 | 34.20 | 23.79 | 28.12 | 29.32 | 32.18 | −0.88 | −26.92 | +12.83 | −55.81 | +9.94 | −25.32 | |
Pantomime (1280 × 960) | 15 | 38.95 | 38.93 | 38.97 | 34.03 | 32.90 | 36.66 | 37.88 | −51.43 | +15.25 | +36.92 | −92.33 | −49.85 | −48.01 |
25 | 36.20 | 36.46 | 36.81 | 29.45 | 31.72 | 32.43 | 35.39 | −44.80 | −7.90 | +26.37 | −62.47 | −64.46 | −58.29 | |
35 | 34.46 | 34.32 | 35.27 | 22.74 | 30.45 | 31.02 | 33.23 | −54.02 | −48.38 | +62.43 | −68.22 | −77.88 | −51.64 | |
Dog (1280 × 960) | 15 | 36.05 | 35.25 | 35.66 | 33.34 | 30.61 | 33.64 | 35.39 | −19.83 | +29.80 | −33.55 | −61.76 | −37.74 | −52.88 |
25 | 33.18 | 32.84 | 33.45 | 30.05 | 30.28 | 30.50 | 33.11 | −1.63 | +4.37 | +16.44 | −64.24 | −57.75 | −60.86 | |
35 | 31.13 | 30.91 | 31.92 | 26.09 | 29.93 | 28.32 | 31.35 | +47.82 | −25.89 | +13.24 | −69.20 | −61.28 | −60.77 | |
Wall (1024 × 768) | 15 | 36.90 | 35.99 | 37.84 | 33.74 | 32.38 | 37.71 | 38.15 | −8.08 | +1.03 | +189.44 | −27.04 | −55.88 | −48.61 |
25 | 33.81 | 33.42 | 35.47 | 31.60 | 32.07 | 34.60 | 35.38 | +19.00 | −36.03 | +406.64 | −33.56 | −75.27 | −65.42 | |
35 | 31.61 | 31.31 | 33.83 | 29.84 | 31.76 | 32.44 | 33.06 | +148.10 | −62.21 | +167.57 | −41.74 | −83.41 | −76.05 | |
Piano (1024 × 768) | 15 | 37.42 | 35.89 | 37.29 | 35.60 | 31.17 | 37.88 | 38.20 | −13.02 | +35.02 | +522.99 | −11.58 | −50.05 | −39.49 |
25 | 34.46 | 33.14 | 34.90 | 32.38 | 30.87 | 34.27 | 35.35 | +10.33 | −4.02 | +131.63 | −19.55 | −72.39 | −55.59 | |
35 | 32.36 | 31.01 | 33.15 | 30.24 | 30.56 | 31.86 | 33.07 | +130.98 | −41.80 | +127.47 | −34.40 | −79.18 | −65.27 | |
Kendo (1024 × 768) | 15 | 37.73 | 35.88 | 38.14 | 33.47 | 31.23 | 36.28 | 36.71 | +5.70 | −23.47 | +1891.84 | −37.20 | −39.03 | −28.46 |
25 | 34.82 | 33.13 | 36.21 | 30.14 | 30.95 | 33.11 | 34.68 | +47.63 | −43.35 | +2218.42 | −46.27 | −41.51 | −39.68 | |
35 | 32.69 | 30.97 | 34.81 | 27.83 | 30.66 | 30.71 | 33.05 | +162.93 | −58.75 | +1902.10 | −57.39 | −41.56 | −47.12 | |
Balloons (1024 × 768) | 15 | 36.84 | 34.56 | 36.91 | 32.12 | 31.13 | 34.86 | 36.88 | +3.10 | −14.68 | +1071.66 | −37.88 | −21.85 | −30.60 |
25 | 33.96 | 31.92 | 34.77 | 28.97 | 30.54 | 31.73 | 34.03 | +37.70 | −30.12 | +1220.68 | −45.56 | −30.30 | −42.88 | |
35 | 31.92 | 29.86 | 33.27 | 26.80 | 30.14 | 28.42 | 32.30 | +100.48 | −47.50 | +1060.70 | −54.66 | −35.73 | −49.21 |
2D Sequence | σ | PSNR (dB) | BDBR (%) | ||
---|---|---|---|---|---|
LFBM5D | Proposed | LFBM5D | Proposed | ||
Ohta (384 × 288) | 15 | 33.58 | 34.17 | −40.00 | −25.93 |
25 | 29.79 | 31.21 | −46.15 | −30.24 | |
35 | 27.52 | 29.31 | −43.21 | −31.39 | |
Ohta2 (384 × 288) | 15 | 29.71 | 33.64 | +40.45 | −10.75 |
25 | 26.45 | 30.99 | +84.44 | −11.18 | |
35 | 24.40 | 29.21 | +129.02 | −10.14 | |
Sideboard (512 × 512) | 15 | 34.08 | 33.92 | −48.22 | −20.90 |
25 | 30.91 | 30.63 | −60.90 | −36.17 | |
35 | 28.77 | 27.15 | −69.21 | −40.67 |
Sequence | σ | PSNR (dB) | |||
---|---|---|---|---|---|
BM3D | MS-EPLL | MVCNN | Proposed | ||
Champagne (640 × 480) | 15 | 36.33 | 36.02 | 19.59 | 35.83 |
25 | 33.70 | 33.37 | 18.97 | 33.36 | |
35 | 31.52 | 31.62 | 18.19 | 31.37 | |
Pantomime (640 × 480) | 15 | 37.35 | 37.22 | 24.00 | 37.20 |
25 | 34.73 | 34.63 | 22.78 | 34.51 | |
35 | 32.40 | 32.88 | 21.25 | 32.27 | |
Dog (640 × 480) | 15 | 34.24 | 33.41 | 23.48 | 34.44 |
25 | 31.81 | 30.84 | 22.15 | 31.85 | |
35 | 29.99 | 29.10 | 20.84 | 29.89 | |
Wall (512 × 384) | 15 | 37.34 | 36.30 | 24.57 | 37.76 |
25 | 34.58 | 33.71 | 23.04 | 34.47 | |
35 | 32.57 | 32.04 | 21.37 | 32.11 | |
Piano (512 × 384) | 15 | 36.75 | 35.43 | 24.36 | 37.98 |
25 | 33.88 | 32.70 | 22.77 | 34.66 | |
35 | 31.85 | 30.95 | 21.09 | 32.34 | |
Kendo (512 × 384) | 15 | 34.29 | 36.40 | 25.36 | 33.74 |
25 | 30.93 | 34.01 | 23.66 | 30.92 | |
35 | 29.59 | 32.37 | 21.92 | 29.63 | |
Balloons (512 × 384) | 15 | 32.94 | 34.79 | 24.16 | 32.72 |
25 | 31.20 | 32.22 | 22.61 | 31.27 | |
35 | 28.44 | 30.51 | 21.04 | 29.70 | |
Cotton (512 × 512) | 15 | 30.30 | 30.77 | −47.96 | −47.01 |
25 | 29.48 | 30.56 | −58.82 | −63.30 | |
35 | 28.57 | 30.02 | −60.34 | −77.45 |
Sequence | σ | BDBR (%) | ||
---|---|---|---|---|
MS-EPLL | MVCNN | Proposed | ||
Champagne (640 × 480) | 15 | +15.94 | −79.30 | −8.97 |
25 | +7.31 | −84.80 | −15.52 | |
35 | −13.03 | −88.78 | −20.52 | |
Pantomime (640 × 480) | 15 | +36.13 | −69.17 | −21.72 |
25 | +19.17 | −81.63 | −28.50 | |
35 | −16.65 | −89.32 | −33.21 | |
Dog (640 × 480) | 15 | +80.70 | −85.09 | −28.40 |
25 | +50.12 | −91.92 | −35.59 | |
35 | +0.67 | −94.77 | −35.37 | |
Wall (512 × 384) | 15 | +43.53 | −63.18 | −21.32 |
25 | +7.02 | −80.57 | −30.45 | |
35 | −30.01 | −89.55 | −29.00 | |
Piano (512 × 384) | 15 | +67.55 | −65.07 | −36.21 |
25 | +18.96 | −83.73 | −48.27 | |
35 | −29.12 | −91.97 | −47.86 | |
Kendo (512 × 384) | 15 | +3.15 | −58.87 | −22.19 |
25 | −40.66 | −80.85 | −21.46 | |
35 | −37.12 | −70.09 | −10.17 | |
Balloons (512 × 384) | 15 | +14.29 | −60.74 | −8.91 |
25 | −7.93 | −74.25 | −16.88 | |
35 | −46.64 | −86.79 | −20.83 |
Sequence | σ | BDBR (%) | |
---|---|---|---|
Only Spatial | Spatial + Temporal | ||
Champagne (1280 × 960) | 15 | −9.23 | −14.97 |
25 | −19.32 | −20.98 | |
35 | −25.38 | −25.32 | |
Pantomime (1280 × 960) | 15 | −30.38 | −48.01 |
25 | −46.67 | −58.29 | |
35 | −55.87 | −51.64 | |
Dog (1280 × 960) | 15 | −35.00 | −52.88 |
25 | −16.43 | −60.86 | |
35 | −49.92 | −60.77 | |
Wall (1024 × 768) | 15 | −25.42 | −48.61 |
25 | −40.50 | −65.42 | |
35 | −46.90 | −76.05 | |
Piano (1024 × 768) | 15 | −37.10 | −39.49 |
25 | −57.28 | −55.59 | |
35 | −66.83 | −65.27 | |
Kendo (1024 × 768) | 15 | −22.93 | −28.46 |
25 | −34.98 | −39.68 | |
35 | −39.48 | −47.12 | |
Balloons (1024 × 768) | 15 | −20.55 | −30.60 |
25 | −31.83 | −42.88 | |
35 | −38.02 | −49.21 |
Matlab | C++ | |
---|---|---|
BM3D | 1.00 | 9.00 |
VBM4D | 3.59 | - |
MS-EPLL | 1618.90 | - |
3D-focus | 127.33 | - |
MVCNN | 29.33 | - |
LFBM5D | - | 35.27 |
Proposed | - | 1.80 |
Searching | Number of Patches | |
---|---|---|
BM3D | 100.60 × 107 | 8.49 × 106 |
VBM4D | 951.58 × 107 | 2.21 × 106 |
MS-EPLL | - | - |
3D-focus | 2.06 × 107 | 98.30 × 106 |
MVCNN | - | - |
LFBM5D | 7.86 × 107 | 10.78 × 106 |
Proposed | 0.06 × 107 | 4.34 × 106 |
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Jin, Y.-N.; Rhee, C.-E. Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation. Sensors 2021, 21, 2919. https://doi.org/10.3390/s21092919
Jin Y-N, Rhee C-E. Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation. Sensors. 2021; 21(9):2919. https://doi.org/10.3390/s21092919
Chicago/Turabian StyleJin, You-Na, and Chae-Eun Rhee. 2021. "Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation" Sensors 21, no. 9: 2919. https://doi.org/10.3390/s21092919