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.
Similar content being viewed by others
References
You JY, Chien SI (2008) Saturation enhancement of blue sky for increasing preference of scenery images. IEEE Trans Consum Electron 54(2):762–768
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
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
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
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
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
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
Bhandari AK (2020) A logarithmic law based histogram modification scheme for naturalness image contrast enhancement. J Ambient Intell Humaniz Comput 11(4):1605–1627
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
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
Hema D, Kannan DS (2019) Interactive color image segmentation using HSV color space. Sci Technol J 7(1):37–41
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
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
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
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
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
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
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
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.
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
Chernov V, Alander J, Bochko V (2015) Integer-based accurate conversion between RGB and HSV color spaces. Comput Electr Eng 46:328–337
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
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
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
Pei S, Chiu Y (2006) Background adjustment and saturation enhancement in ancient Chinese paintings. IEEE Trans Image Process 15(10):3230–3234
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
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
Kim SM, You J (2022) Lossless LUT compressions for Image Enhancement. J Semicond Technol Sci 23(3):162–175
Weste NH, Harris D (2015) CMOS VLSI design: a circuits and systems perspective. Pearson Education India, India
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
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
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
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
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
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
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
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
Corresponding author
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.
About this article
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
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42835-024-01859-y