Abstract

Local dimming techniques have been widely studied to achieve a high contrast ratio and low power consumption for liquid crystal displays. The luminance of a backlight is reduced according to some characteristics of an input image and the pixel data are boosted to compensate for the dimmed backlight. In addition, because a backlight block is affected by adjacent ones, the pixel compensation algorithm requires huge processing power as well as many iterations along with the overall luminance profile information of a backlight. However, a proposed deep-learning-based local dimming algorithm generates the compensated image directly from an input image without any information of backlight’s dimming levels. The proposed compensation network is constructed on the basis of the U-net to maintain the high-resolution features in the up-sampling paths through skip-connections. In addition, it is also ensured that the bi-linear interpolation can be used without visible image quality degradation for the reduction on the number of parameters. The proposed networks are trained and verified on a DIV2K 2K image dataset.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. B. Geffroy, P. le Roy, and C. Prat, “Organic light-emitting diode (OLED) technology: materials, devices and display technologies,” Polym. Int. 55 (6), 572–582 (2006).
    [Crossref]
  2. K. Müllen and U. Scherf, Organic light emitting devices: synthesis, properties and applications(John Wiley & Sons, 2006).
  3. H. Cho and O. Kwon, “A local dimming algorithm for low power LCD TVs using edge-type LED backlight,” IEEE Trans. Consum. Electron. 56 (4), 2054–2060 (2010).
    [Crossref]
  4. H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
    [Crossref]
  5. D. M. Hoffman, N. N. Stepien, and W. Xiong, “The importance of native panel contrast and local dimming density on perceived image quality of high dynamic range displays,” J. Soc. Inf. Disp. 24 (4), 216–228 (2016).
    [Crossref]
  6. G. Tan, Y. Huang, M.-C. Li, S.-L. Lee, and S.-T. Wu, “High dynamic range liquid crystal displays with a mini-led backlight,” Opt. Express 26 (13), 16572–16584 (2018).
    [Crossref] [PubMed]
  7. H. Nam and E.-J. Song, “Low color distortion adaptive dimming scheme for power efficient LCDs,” Opt. & Laser Technol. 48, 52–59 (2013).
    [Crossref]
  8. H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
    [Crossref]
  9. H. Cho and O.-K. Kwon, “A backlight dimming algorithm for low power and high image quality LCD applications,” IEEE Trans. Consum. Electron. 55 (2), 839–844 (2009).
    [Crossref]
  10. W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
    [Crossref]
  11. S.-K. Kim, S.-J. Song, and H. Nam, “Bilinear weighting and threshold scheme for low-power two-dimensional local dimming liquid crystal displays without block artifacts,” Opt. Eng. 53 (6), 063110 (2014).
    [Crossref]
  12. E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
    [Crossref] [PubMed]
  13. Z. Luo, Y. Chen, and S.-T. Wu, “Wide color gamut LCD with a quantum dot backlight,” Opt. Express 21, 26269–26284 (2013).
    [Crossref] [PubMed]
  14. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in), Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, Inc., 2012), pp. 1097–1105.
  15. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT, 2016).
  16. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.
  17. J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.
  18. A. E. Orhan and X. Pitkow, “Skip connections eliminate singularities,” arXiv 1701.09175 (2017).
  19. R. Fergus, M. D. Zeiler, G. W. Taylor, and D. Krishnan, “Deconvolutional networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.
  20. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 770–778.
  21. E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, (IEEE, 2017).
  22. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feed forward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, (AISTATS,2010), pp. 249–256.
  23. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference for Learning Representation, (2015).
  24. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.
  25. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
    [Crossref] [PubMed]
  26. W. G. Backhaus, R. Kliegl, and J. S. Werner, Color vision: Perspectives from different disciplines(Walter de Gruyter, 2011).
  27. C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).
  28. Y. Kim, J.-S. Choi, and M. Kim, “A real-time convolutional neural network for super-resolution on fpga with applications to 4k uhd 60 fps video services,” IEEE Trans. Circuits Syst. Video Technol. (to be published).
  29. J.-W. Chang, K.-W. Kang, and S.-J. Kang, “An energy-efficient fpga-based deconvolutional neural networks accelerator for single image super-resolution,” IEEE Trans. Circuits Syst. Video Technol. (to be published).
  30. J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
    [Crossref]
  31. T. Shiga, S. Shimizukawa, and S. Mikoshiba, “Power savings and enhancement of gray-scale capability of lcd tvs with an adaptive dimming technique,” J. Soc. Inf. Disp. 16 (2), 311–316 (2008).
    [Crossref]

2019 (1)

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

2018 (1)

2017 (1)

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

2016 (1)

D. M. Hoffman, N. N. Stepien, and W. Xiong, “The importance of native panel contrast and local dimming density on perceived image quality of high dynamic range displays,” J. Soc. Inf. Disp. 24 (4), 216–228 (2016).
[Crossref]

2014 (1)

S.-K. Kim, S.-J. Song, and H. Nam, “Bilinear weighting and threshold scheme for low-power two-dimensional local dimming liquid crystal displays without block artifacts,” Opt. Eng. 53 (6), 063110 (2014).
[Crossref]

2013 (2)

Z. Luo, Y. Chen, and S.-T. Wu, “Wide color gamut LCD with a quantum dot backlight,” Opt. Express 21, 26269–26284 (2013).
[Crossref] [PubMed]

H. Nam and E.-J. Song, “Low color distortion adaptive dimming scheme for power efficient LCDs,” Opt. & Laser Technol. 48, 52–59 (2013).
[Crossref]

2011 (1)

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

2010 (3)

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

H. Cho and O. Kwon, “A local dimming algorithm for low power LCD TVs using edge-type LED backlight,” IEEE Trans. Consum. Electron. 56 (4), 2054–2060 (2010).
[Crossref]

2009 (1)

H. Cho and O.-K. Kwon, “A backlight dimming algorithm for low power and high image quality LCD applications,” IEEE Trans. Consum. Electron. 55 (2), 839–844 (2009).
[Crossref]

2008 (1)

T. Shiga, S. Shimizukawa, and S. Mikoshiba, “Power savings and enhancement of gray-scale capability of lcd tvs with an adaptive dimming technique,” J. Soc. Inf. Disp. 16 (2), 311–316 (2008).
[Crossref]

2007 (1)

H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
[Crossref]

2006 (1)

B. Geffroy, P. le Roy, and C. Prat, “Organic light-emitting diode (OLED) technology: materials, devices and display technologies,” Polym. Int. 55 (6), 572–582 (2006).
[Crossref]

2004 (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
[Crossref] [PubMed]

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Agustsson, E.

E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, (IEEE, 2017).

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference for Learning Representation, (2015).

Backhaus, W. G.

W. G. Backhaus, R. Kliegl, and J. S. Werner, Color vision: Perspectives from different disciplines(Walter de Gruyter, 2011).

Barham, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Bengio, Y.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feed forward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, (AISTATS,2010), pp. 249–256.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT, 2016).

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

Chang, J.-W.

J.-W. Chang, K.-W. Kang, and S.-J. Kang, “An energy-efficient fpga-based deconvolutional neural networks accelerator for single image super-resolution,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

Chen, H.

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
[Crossref]

Chen, J.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Chen, Y.

Chen, Z.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Cho, H.

H. Cho and O. Kwon, “A local dimming algorithm for low power LCD TVs using edge-type LED backlight,” IEEE Trans. Consum. Electron. 56 (4), 2054–2060 (2010).
[Crossref]

H. Cho and O.-K. Kwon, “A backlight dimming algorithm for low power and high image quality LCD applications,” IEEE Trans. Consum. Electron. 55 (2), 839–844 (2009).
[Crossref]

Choi, J.-S.

Y. Kim, J.-S. Choi, and M. Kim, “A real-time convolutional neural network for super-resolution on fpga with applications to 4k uhd 60 fps video services,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

Chou, Q.-Y.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Courville, A.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT, 2016).

Davis, A.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Dean, J.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Devin, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

El-Alfy, E.-S. M.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Fergus, R.

R. Fergus, M. D. Zeiler, G. W. Taylor, and D. Krishnan, “Deconvolutional networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

Geffroy, B.

B. Geffroy, P. le Roy, and C. Prat, “Organic light-emitting diode (OLED) technology: materials, devices and display technologies,” Polym. Int. 55 (6), 572–582 (2006).
[Crossref]

Ghemawat, S.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Glorot, X.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feed forward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, (AISTATS,2010), pp. 249–256.

Gong, L.

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

Goodfellow, I.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT, 2016).

Ha, T.

H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
[Crossref]

Ha, T. H.

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

Han, B. H.

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 770–778.

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in), Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, Inc., 2012), pp. 1097–1105.

Hoffman, D. M.

D. M. Hoffman, N. N. Stepien, and W. Xiong, “The importance of native panel contrast and local dimming density on perceived image quality of high dynamic range displays,” J. Soc. Inf. Disp. 24 (4), 216–228 (2016).
[Crossref]

Huang, W.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Huang, Y.

Irving, G.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Isard, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Jang, E.

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

Jang, H.

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

Jin, Z.-L.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Jun, S.

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

Kang, K.-W.

J.-W. Chang, K.-W. Kang, and S.-J. Kang, “An energy-efficient fpga-based deconvolutional neural networks accelerator for single image super-resolution,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

Kang, S.

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

Kang, S.-J.

J.-W. Chang, K.-W. Kang, and S.-J. Kang, “An energy-efficient fpga-based deconvolutional neural networks accelerator for single image super-resolution,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

Kim, B.

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

Kim, C.

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

Kim, H. R.

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

Kim, M.

Y. Kim, J.-S. Choi, and M. Kim, “A real-time convolutional neural network for super-resolution on fpga with applications to 4k uhd 60 fps video services,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

Kim, S.

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

Kim, S.-K.

S.-K. Kim, S.-J. Song, and H. Nam, “Bilinear weighting and threshold scheme for low-power two-dimensional local dimming liquid crystal displays without block artifacts,” Opt. Eng. 53 (6), 063110 (2014).
[Crossref]

Kim, Y.

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

Y. Kim, J.-S. Choi, and M. Kim, “A real-time convolutional neural network for super-resolution on fpga with applications to 4k uhd 60 fps video services,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

Kingma, D. P.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference for Learning Representation, (2015).

Kliegl, R.

W. G. Backhaus, R. Kliegl, and J. S. Werner, Color vision: Perspectives from different disciplines(Walter de Gruyter, 2011).

Krishnan, D.

R. Fergus, M. D. Zeiler, G. W. Taylor, and D. Krishnan, “Deconvolutional networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in), Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, Inc., 2012), pp. 1097–1105.

Kudlur, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Kurita, E. D.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Kuwashima, S.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Kwon, O.

H. Cho and O. Kwon, “A local dimming algorithm for low power LCD TVs using edge-type LED backlight,” IEEE Trans. Consum. Electron. 56 (4), 2054–2060 (2010).
[Crossref]

Kwon, O.-K.

H. Cho and O.-K. Kwon, “A backlight dimming algorithm for low power and high image quality LCD applications,” IEEE Trans. Consum. Electron. 55 (2), 839–844 (2009).
[Crossref]

le Roy, P.

B. Geffroy, P. le Roy, and C. Prat, “Organic light-emitting diode (OLED) technology: materials, devices and display technologies,” Polym. Int. 55 (6), 572–582 (2006).
[Crossref]

Lee, J.

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

Lee, S.-L.

Levenberg, J.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Li, F.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Li, J.-M.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Li, M.-C.

Li, X.

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

Li, Y.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Lim, J.

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

Liu, Y.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Luo, Z.

Mikoshiba, S.

T. Shiga, S. Shimizukawa, and S. Mikoshiba, “Power savings and enhancement of gray-scale capability of lcd tvs with an adaptive dimming technique,” J. Soc. Inf. Disp. 16 (2), 311–316 (2008).
[Crossref]

Monga, R.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Moore, S.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Müllen, K.

K. Müllen and U. Scherf, Organic light emitting devices: synthesis, properties and applications(John Wiley & Sons, 2006).

Murray, D. G.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Nam, H.

S.-K. Kim, S.-J. Song, and H. Nam, “Bilinear weighting and threshold scheme for low-power two-dimensional local dimming liquid crystal displays without block artifacts,” Opt. Eng. 53 (6), 063110 (2014).
[Crossref]

H. Nam and E.-J. Song, “Low color distortion adaptive dimming scheme for power efficient LCDs,” Opt. & Laser Technol. 48, 52–59 (2013).
[Crossref]

Orhan, A. E.

A. E. Orhan and X. Pitkow, “Skip connections eliminate singularities,” arXiv 1701.09175 (2017).

Park, Y.

H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
[Crossref]

Pitkow, X.

A. E. Orhan and X. Pitkow, “Skip connections eliminate singularities,” arXiv 1701.09175 (2017).

Prat, C.

B. Geffroy, P. le Roy, and C. Prat, “Organic light-emitting diode (OLED) technology: materials, devices and display technologies,” Polym. Int. 55 (6), 572–582 (2006).
[Crossref]

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 770–778.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

Scherf, U.

K. Müllen and U. Scherf, Organic light emitting devices: synthesis, properties and applications(John Wiley & Sons, 2006).

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
[Crossref] [PubMed]

Shiga, T.

T. Shiga, S. Shimizukawa, and S. Mikoshiba, “Power savings and enhancement of gray-scale capability of lcd tvs with an adaptive dimming technique,” J. Soc. Inf. Disp. 16 (2), 311–316 (2008).
[Crossref]

Shimizukawa, S.

T. Shiga, S. Shimizukawa, and S. Mikoshiba, “Power savings and enhancement of gray-scale capability of lcd tvs with an adaptive dimming technique,” J. Soc. Inf. Disp. 16 (2), 311–316 (2008).
[Crossref]

Shin, D.

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
[Crossref] [PubMed]

Song, E.-J.

H. Nam and E.-J. Song, “Low color distortion adaptive dimming scheme for power efficient LCDs,” Opt. & Laser Technol. 48, 52–59 (2013).
[Crossref]

Song, S.-J.

S.-K. Kim, S.-J. Song, and H. Nam, “Bilinear weighting and threshold scheme for low-power two-dimensional local dimming liquid crystal displays without block artifacts,” Opt. Eng. 53 (6), 063110 (2014).
[Crossref]

Steiner, B.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Stepien, N. N.

D. M. Hoffman, N. N. Stepien, and W. Xiong, “The importance of native panel contrast and local dimming density on perceived image quality of high dynamic range displays,” J. Soc. Inf. Disp. 24 (4), 216–228 (2016).
[Crossref]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 770–778.

Sung, J.

H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
[Crossref]

Sung, J. H.

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in), Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, Inc., 2012), pp. 1097–1105.

Takio,

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Tan, G.

Taylor, G. W.

R. Fergus, M. D. Zeiler, G. W. Taylor, and D. Krishnan, “Deconvolutional networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

Timofte, R.

E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, (IEEE, 2017).

Tucker, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Vasudevan, V.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Wang, C.

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
[Crossref] [PubMed]

Warden, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Werner, J. S.

W. G. Backhaus, R. Kliegl, and J. S. Werner, Color vision: Perspectives from different disciplines(Walter de Gruyter, 2011).

Wicke, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Wu, S.-T.

Xie, S.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Xie, Y.

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

Xiong, W.

D. M. Hoffman, N. N. Stepien, and W. Xiong, “The importance of native panel contrast and local dimming density on perceived image quality of high dynamic range displays,” J. Soc. Inf. Disp. 24 (4), 216–228 (2016).
[Crossref]

Yamanaka, J.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Yang, L.-M.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Yoo, H.-J.

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

Yu, Q.

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

Yu, Y.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Zeiler, M. D.

R. Fergus, M. D. Zeiler, G. W. Taylor, and D. Krishnan, “Deconvolutional networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 770–778.

Zhao, D.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

Zheng, X.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

Zhong, Z.-G.

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Zhou, X.

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

Adv. Mater. (1)

E. Jang, S. Jun, H. Jang, J. Lim, B. Kim, and Y. Kim, “White-light-emitting diodes with quantum dot color converters for display backlights,” Adv. Mater. 22 (28), 3076–3080 (2010).
[Crossref] [PubMed]

IEEE J. Solid-State Circuits (1)

J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, “Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,” IEEE J. Solid-State Circuits 54 (1), 173–185 (2019).
[Crossref]

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (1)

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “Dlau: A scalable deep learning accelerator unit on fpga,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36 (3), 513–517 (2017).

IEEE Trans. Consum. Electron. (2)

H. Cho and O. Kwon, “A local dimming algorithm for low power LCD TVs using edge-type LED backlight,” IEEE Trans. Consum. Electron. 56 (4), 2054–2060 (2010).
[Crossref]

H. Cho and O.-K. Kwon, “A backlight dimming algorithm for low power and high image quality LCD applications,” IEEE Trans. Consum. Electron. 55 (2), 839–844 (2009).
[Crossref]

IEEE Trans. Image Process. (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004).
[Crossref] [PubMed]

J. Soc. Inf. Disp. (4)

T. Shiga, S. Shimizukawa, and S. Mikoshiba, “Power savings and enhancement of gray-scale capability of lcd tvs with an adaptive dimming technique,” J. Soc. Inf. Disp. 16 (2), 311–316 (2008).
[Crossref]

H. Chen, T. H. Ha, J. H. Sung, H. R. Kim, and B. H. Han, “Evaluation of LCD local-dimming-backlight system,” J. Soc. Inf. Disp. 18 (1), 57–65 (2010).
[Crossref]

H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV,” J. Soc. Inf. Disp. 15 (12), 981–988 (2007).
[Crossref]

D. M. Hoffman, N. N. Stepien, and W. Xiong, “The importance of native panel contrast and local dimming density on perceived image quality of high dynamic range displays,” J. Soc. Inf. Disp. 24 (4), 216–228 (2016).
[Crossref]

Opt. & Laser Technol. (2)

H. Nam and E.-J. Song, “Low color distortion adaptive dimming scheme for power efficient LCDs,” Opt. & Laser Technol. 48, 52–59 (2013).
[Crossref]

W. Huang, J.-M. Li, L.-M. Yang, Z.-L. Jin, Z.-G. Zhong, Y. Liu, Q.-Y. Chou, and F. Li, “Local dimming algorithm and color gamut calibration for RGB LED backlight LCD display,” Opt. & Laser Technol. 43 (1), 214–217 (2011).
[Crossref]

Opt. Eng. (1)

S.-K. Kim, S.-J. Song, and H. Nam, “Bilinear weighting and threshold scheme for low-power two-dimensional local dimming liquid crystal displays without block artifacts,” Opt. Eng. 53 (6), 063110 (2014).
[Crossref]

Opt. Express (2)

Polym. Int. (1)

B. Geffroy, P. le Roy, and C. Prat, “Organic light-emitting diode (OLED) technology: materials, devices and display technologies,” Polym. Int. 55 (6), 572–582 (2006).
[Crossref]

Other (15)

K. Müllen and U. Scherf, Organic light emitting devices: synthesis, properties and applications(John Wiley & Sons, 2006).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in), Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. (Curran Associates, Inc., 2012), pp. 1097–1105.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT, 2016).

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

J. Yamanaka, S. Kuwashima, E. D. Kurita, Takio, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, “Fast and accurate image super resolution by deep CNN with skip connection and network in network,” in Neural Information Processing, (Springer, 2017), pp. 217–225.

A. E. Orhan and X. Pitkow, “Skip connections eliminate singularities,” arXiv 1701.09175 (2017).

R. Fergus, M. D. Zeiler, G. W. Taylor, and D. Krishnan, “Deconvolutional networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2016), pp. 770–778.

E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, (IEEE, 2017).

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feed forward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, (AISTATS,2010), pp. 249–256.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference for Learning Representation, (2015).

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation, (USENIX, 2016), pp. 265–283.

W. G. Backhaus, R. Kliegl, and J. S. Werner, Color vision: Perspectives from different disciplines(Walter de Gruyter, 2011).

Y. Kim, J.-S. Choi, and M. Kim, “A real-time convolutional neural network for super-resolution on fpga with applications to 4k uhd 60 fps video services,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

J.-W. Chang, K.-W. Kang, and S.-J. Kang, “An energy-efficient fpga-based deconvolutional neural networks accelerator for single image super-resolution,” IEEE Trans. Circuits Syst. Video Technol. (to be published).

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Figures (11)

Fig. 1
Fig. 1 Conventional local dimming process for LCDs.
Fig. 2
Fig. 2 Overall architecture of the proposed local dimming system. The proposed LDNN can be implemented (a) in a TV-set side or (b) in an LCD-module side.
Fig. 3
Fig. 3 The proposed LDNN architecture with a sandglass shape. The upper blue paths denote the skip-connections that concatenate the data of convolution layers to the up-sampling layers. On the other hand, the lower black arrows represent the skip-connections leading to addition. Layers marked in blue are strided convolution layers for down-sampling and layers marked in green are strided transposed convolution layers for up-sampling, respectively. Other convolution layers are implemented with the stride of 1. The numbers above each layer denote the spatial resolution of each convolution layer.
Fig. 4
Fig. 4 Residual block (a) with a direct skip-connection (b) with a skip-connection through a 1 × 1 convolution layer.
Fig. 5
Fig. 5 2-dimensional bi-linear interpolation for up-sampling layers.
Fig. 6
Fig. 6 sLDNN architecture. The yellow boxes denote bi-linear interpolation. Because the concatenation increases the number of channels in the up-sampling network and interpolations increase only the spatial resolution of each channel, RBs with 1 × 1 convolution layer (RB(b)) are used, while RBs with direct skip-connections (RB(a)) are adopted in the down-sampling network.
Fig. 7
Fig. 7 The training process of the proposed LDNN.
Fig. 8
Fig. 8 LSF models (a) for red/green LED sub-blocks and (b) for blue LED sub-blocks. The horizontal axis is normalized by the size of a BLU sub-block.
Fig. 9
Fig. 9 Test result examples of the proposed LDNN for three input images.
Fig. 10
Fig. 10 Performance evaluation results of WPC [11], LDNN, and sLDNN. Red box regions are separately shown for the clearer comparison of algorithms. Power is the BLU power consumption ratio for the fully turned-on BLU power and GT stands for ground truth.
Fig. 11
Fig. 11 Output image examples of sLDNN networks with skip-connections and without skip-connections on test images. Both networks are separately trained with same training images.

Tables (4)

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Table 1 The comparison of LDNN and sLDNN.

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Table 2 Evaluation summary over test images.

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Table 3 Evaluation summary of LDNN and sLDNN at 36×48 BLU sub-blocks.

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Table 4 Average performance comparison of sLDNN networks with and without skip-connections on test images.

Equations (2)

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L S F ( x , y ) = a e ( x m x ) 2 + ( y m y ) 2 σ 1 2 + ( 1 a ) e ( x m x ) 2 + ( y m y ) 2 σ 2 2
L = x = 1 1920 y = 1 1080 ( I I N ( x , y ) I O U T ( x , y ) ) 2 / 1920 / 1080 / 3

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