Skip to main content
Log in

Efficient LUT Design Methodologies of Transformation between RGB and HSV for HSV Based Image Enhancements

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

Algorithms and architectures are presented for efficiently converting RGB to HSV color space and HSV to RGB color space using lookup tables (LUT) to enhance images in widely used HSV color space. These approaches can reduce hardware complexity and improve image qualities by processing complex operations such as division and multiplication with a simpler shared LUT compared to conventional approaches. LUTs for division operations are shared for RGB ↔ HSV conversion and HSV color space image enhancement units with a pipeline to maximize semiconductor chip area efficiency. Methods to reduce LUT counts are discussed based on the various LUT curves for each pixel range. The proposed RGB ↔ HSV algorithms and architectures are verified for skin tone detection and image enhancement in HSV color space. Finally, the performances of the proposed LUT compression methods are evaluated to show that only around 1% LUTs are required with around 5 times higher throughput compared with conventional methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. You JY, Chien SI (2008) Saturation enhancement of blue sky for increasing preference of scenery images. IEEE Trans Consum Electron 54(2):762–768

    Article  Google Scholar 

  2. Shin-Tai Lo, Ruey-Shing Weng, Ching-Fu Hsu (2006) Image Processing Device and Method for Enhancing the Luminance and the Image Quality of Display Panels, US patent no. US2006/0146351

  3. Lee SL, Tseng CC (2017) Color image enhancement using histogram equalization method without changing hue and saturation. In: 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp 305–306. IEEE

  4. Malik R, Dhir R, Mittal SK (2019) Remote sensing and landsat image enhancement using multiobjective PSO based local detail enhancement. J Ambient Intell Humaniz Comput 10:3563–3571

    Article  Google Scholar 

  5. Supriya M, Deepa AJ, Mythili C (2021) Mamographic image for breast cancer detection and identification of stages of cancer using MFFC and optimized ANFIS. J Ambient Intell Humaniz Comput 12:8731–8745

    Article  Google Scholar 

  6. Tao P, Pei Y, Celenk M, Fu Q, Wu A (2020) Adaptive image enhancement method using contrast limitation based on multiple layers BOHE. J Ambient Intell Humaniz Comput 11:5031–5043

    Article  Google Scholar 

  7. Sharma R, Ravinder M, Sharma N, Sharma K (2022) An optimal remote sensing image enhancement with weak detail preservation in wavelet domain. J Ambient Intell Humaniz Comput 13:1–12

    Article  Google Scholar 

  8. Bhandari AK (2020) A logarithmic law based histogram modification scheme for naturalness image contrast enhancement. J Ambient Intell Humaniz Comput 11(4):1605–1627

    Article  Google Scholar 

  9. Islam IU, Ullah K, Afaq M, Chaudary MH, Hanif MK (2019) Spatio-temporal sEMG image enhancement and motor unit action potential (MUAP) detection: algorithms and their analysis. J Ambient Intell Humaniz Comput 10:3809–3819

    Article  Google Scholar 

  10. Adurkar A, Patel P, Sabnis, M, Patil P (2022) Fire Detection using HSV Color Picker: A Review. In: 2022 5th International Conference on Advances in Science and Technology (ICAST), pp 450–455. IEEE

  11. Hema D, Kannan DS (2019) Interactive color image segmentation using HSV color space. Sci Technol J 7(1):37–41

    Article  Google Scholar 

  12. Bora DJ (2017) A novel approach for color image edge detection using multidirectional Sobel filter on HSV color space. Int J Comput Sci Eng 5(2):154–159

    Google Scholar 

  13. Ajmal A, Hollitt C, Frean M, Al-Sahaf H (2018) A comparison of RGB and HSV colour spaces for visual attention models. In: 2018 International conference on image and vision computing New Zealand (IVCNZ), pp 1–6. IEEE

  14. Amri H, Khalfallah LJC, Bouhlel MS (2017) REPro JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images. Imaging Sci J 65(2):98–107

    Article  Google Scholar 

  15. Chaves-González JM, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM (2010) Detecting skin in face recognition systems: a colour spaces study. Digit signal process 20(3):806–823

    Article  Google Scholar 

  16. Lai CC, Tsai CC (2008) Backlight power reduction and image contrast enhancement using adaptive dimming for global backlight applications. IEEE Trans Consum Electron 54(2):669–674

    Article  Google Scholar 

  17. Zhang MZ, Seow MJ, Tao L, Asari VK (2008) A tunable high-performance architecture for enhancement of stream video captured under non-uniform lighting conditions. Microprocess Microsyst 32(7):386–393

    Article  Google Scholar 

  18. Liu Y, Zhang Y, Zhang C (2015) A fast algorithm for YCbCr to perception color model conversion based on fixed-point DSP. Multimed T Appl 74:6041–6067

    Article  Google Scholar 

  19. Hanumantharaju MC, Ravishankar M, Rameshbabu DR, Ramachandran, S (2011) A novel FPGA implementation of adaptive color image enhancement based on HSV color space. In: 2011 3rd International Conference on Electronics Computer Technology,Vol 2, pp 160–163. IEEE.

  20. Saravanan G, Yamuna G, Nandhini S (2016) Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. In 2016 International Conference on Communication and Signal Processing (ICCSP), pp 0462–0466. IEEE

  21. Chernov V, Alander J, Bochko V (2015) Integer-based accurate conversion between RGB and HSV color spaces. Comput Electr Eng 46:328–337

    Article  Google Scholar 

  22. Brusey, Padgham L (2000) Techniques for obtaining robust, real-time, colour-based vision for robotics. In: RoboCup-99: Robot Soccer World Cup III 3, pp 243–253. Springer Berlin Heidelberg

  23. Akita J (2000). Real-time color detection system using custom lsi for high-speed machine vision. In: RoboCup-99: Robot Soccer World Cup III 3 pp 128–135. Springer Berlin Heidelberg

  24. Lee DJ (2000) Color space conversion for linear color grading. In: Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision Vol 4197, pp 358–366. SPIE

  25. Pei S, Chiu Y (2006) Background adjustment and saturation enhancement in ancient Chinese paintings. IEEE Trans Image Process 15(10):3230–3234

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  26. Song G, Qiao XL (2008) Color image enhancement based on luminance and saturation components. In: 2008 congress on image and signal processing Vol 3, pp 307–310. IEEE

  27. Yu D, Ma LH, Lu HQ (2007) Normalized SI correction for hue-preserving color image enhancement. In: 2007 International conference on machine learning and cybernetics Vol 3, pp 1498–1503. IEEE

  28. Kim SM, You J (2022) Lossless LUT compressions for Image Enhancement. J Semicond Technol Sci 23(3):162–175

    Article  Google Scholar 

  29. Weste NH, Harris D (2015) CMOS VLSI design: a circuits and systems perspective. Pearson Education India, India

    Google Scholar 

  30. Ngo HT, Zhang M, Tao L, Asari VK (2009) Design of a high performance architecture for real-time enhancement of video stream captured in extremely low lighting environment. Microprocess Microsyst 33(4):273–280

    Article  Google Scholar 

  31. Zhang MZ, Seow MJ, Asari VK (2006) A high performance architecture for color image enhancement using a machine learning approach. Int J Comput Intell Res Spec Issue Adv Neural Netw 2(1):40–47

    Google Scholar 

  32. Ahmed E, Rose J (2000) The effect of LUT and cluster size on deep-submicron FPGA performance and density. In: Proceedings of the 2000 ACM/SIGDA eighth international symposium on Field programmable gate arrays pp 3–12

  33. Iqbal K, Odetayo MO, James A (2014) Face detection of ubiquitous surveillance images for biometric security from an image enhancement perspective. J Ambient Intell Humaniz Comput 5:133–146

    Article  Google Scholar 

  34. Yang J, Fu Z, Tan T, Hu W (2004). Adaptive skin detection using multiple cues. In: 2004 International Conference on Image Processing, 2004. ICIP'04. Vol 2, pp 901–904. IEEE

  35. Niu L, Li W (2006) Color edge detection based on direction information measure. In: 2006 6th World Congress on Intelligent Control and Automation Vol 2, pp 9533–9536. IEEE

  36. Chen B, Lei Y (2004) Indoor and outdoor people detection and shadow suppression by exploiting HSV color information. In: The Fourth International Conference on Computer and Information Technology, 2004. CIT'04. pp 137–142. IEEE

Download references

Acknowledgements

This work was supported by project for ‘Customized technology partner’ by funded Korea Ministry of SMEs and Startups in 2023 (project No. RS-2023-00282321). I would like to express my gratitude to Hyeseong Lee for his assistance with data analysis.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jaehee You.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, S., You, J. Efficient LUT Design Methodologies of Transformation between RGB and HSV for HSV Based Image Enhancements. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01859-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42835-024-01859-y

Keywords

Navigation