A tunable high-performance architecture for enhancement of stream video captured under non-uniform lighting conditions

  • Authors:

  • Ming Z. Zhang;Ming-Jung Seow;Li Tao;Vijayan K. Asari

  • Affiliations:

  • Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA;Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA;Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA;Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA

  • Venue:
  • Microprocessors & Microsystems
  • Year:
  • 2008

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Abstract

A novel architecture for performing hue-saturation-value (HSV) domain enhancement of digital color images captured under non-uniform lighting conditions is proposed in this paper for video streaming applications. The approach promotes log-domain computation to eliminate all multiplications, divisions and exponentiations utilizing the compact high-speed logarithmic estimation modules. An optimized quadrant symmetric architecture is incorporated into the design of homomorphic filter for the enhancement of intensity value. Efficient modules are also presented for conversion between RGB and HSV color spaces with tunable H and S components in HSV for more flexible color rendering. The design is able to bring out details hidden in shadow regions of the image and preserve the bright parts with adjustable vividness and color shift for improvement of visual quality while maintaining its consistency. It is capable of producing 187.86 million outputs per second (MOPs) on Xilinx's Virtex II XC2V2000-4ff896 field programmable gate array (FPGA) at a clock frequency of 187.86MHz. It can process over 179.1 (1024x1024) frames per second, which is very suitable for high definition videos, and consumes approximately 70.7% and 76.8% less hardware resource with 127% and 280% performance boost when compared to the designs with machine learning algorithm in [M.Z. Zhang, M.J. Seow, V.K. Asari, A high performance architecture for color image enhancement using a machine learning approach, International Journal of Computational Intelligence Research - Special Issue on Advances in Neural Networks 2(1) (2006) 40-47], and with separated dynamic and contrast enhancements in [H.T. Ngo, M.Z. Zhang, L. Tao, V.K. Asari, Design of a high performance architecture for real-time enhancement of video stream captured in extremely low lighting environment, International Journal of Embedded Systems: Special Issue on Media and Stream Processing, in press], respectively. This approach also provide 83.4 times performance gain with more consistent fidelity in the results compared to some DSP based implementations (256x256 frame size) [G.D. Hines, Z. Rahman, D.J. Jobson, G.A. Woodell, DSP implementation of the retinex image enhancement algorithm, visual information processing XIII, in: Proceedings of the SPIE, vol. 5438, 2004, pp. 13-24; G.D. Hines, Z. Rahman, D.J. Jobson, G.A. Woodell, Single-scale retinex using digital signal processors, in: Proceedings of the Global Signal Processing Conference, September 2004, pp. 1-6] under the reflectance-illuminance category of image enhancement models.