Low-cost and high-speed hardware implementation of contrast-preserving image dynamic range compression for full-HD video enhancement

This study presents a cost-efficient and high-performance field programmable gate array (FPGA)-based hardware implementation of a contrast-preserving image dynamic range compression algorithm, which is an important function used in modern digital video cameras and displays to improve visual quality of standard dynamic range colour images (8 bits/channel). To achieve this purpose, a hardware-friendly approximation to an existing fast dynamic range compression with local contrast preservation (FDRCLCP) algorithm is proposed. The computation of the proposed approximated FDRCLCP algorithm requires only fixed-point unsigned binary addition, multiplication, and bit-shifting. Moreover, the proposed hardware implementation uses a line buffer instead of a frame buffer to process whole image data. These advantages significantly improve throughput performance and reduce memory requirement of the system. The FPGA implementation of the proposed algorithm requires only about 98 K bits on-chip memory and achieves about 170.24 MHz operating frequency by using an Altera Cyclone II device. This is a large improvement compared with the existing results as it is quick enough to process full high-definition videos (1920 × 1080 pixels) at least 80 frames per second using a low-cost FPGA device.

[1]  Sheng-Jyh Wang,et al.  Bayesian Structure-Preserving Image Contrast Enhancement and its Simplification , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  A Hurlbert,et al.  Formal connections between lightness algorithms. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[3]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[4]  Vijayan K. Asari,et al.  Adaptive and integrated neighborhood-dependent approach for nonlinear enhancement of color images , 2005, J. Electronic Imaging.

[5]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[6]  Erik Reinhard,et al.  Photographic tone reproduction for digital images , 2002, ACM Trans. Graph..

[7]  Luca Fanucci,et al.  Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing , 2007, Journal of Real-Time Image Processing.

[8]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[9]  Hiroaki Kotera,et al.  Dynamic range compression preserving local image contrast for digital video camera , 2005, IEEE Transactions on Consumer Electronics.

[10]  A. Hurlbert The Computation of Color , 1989 .

[11]  T. Poggio,et al.  Synthesizing a color algorithm from examples. , 1988, Science.

[12]  Rodney M. Goodman,et al.  A real-time neural system for color constancy , 1991, IEEE Trans. Neural Networks.

[13]  Chryssanthi Iakovidou,et al.  FPGA implementation of a real-time biologically inspired image enhancement algorithm , 2008, Journal of Real-Time Image Processing.

[14]  Ning Xu,et al.  Intra-and-Inter-Constraint-Based Video Enhancement Based on Piecewise Tone Mapping , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Takahiko Horiuchi,et al.  HDR Image Quality Enhancement Based on Spatially Variant Retinal Response , 2010, EURASIP J. Image Video Process..

[16]  E H Land,et al.  Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Firas Hassan,et al.  A real-time implementation of gradient domain high dynamic range compression using a local Poisson solver , 2013, Journal of Real-Time Image Processing.

[18]  Ching-Te Chiu,et al.  BiTA/SWCE: Image Enhancement With Bilateral Tone Adjustment and Saliency Weighted Contrast Enhancement , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  E H Land,et al.  An alternative technique for the computation of the designator in the retinex theory of color vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Chi-Yi Tsai,et al.  A Fast Dynamic Range Compression With Local Contrast Preservation Algorithm and Its Application to Real-Time Video Enhancement , 2012, IEEE Transactions on Multimedia.

[21]  Sangkeun Lee,et al.  An Efficient Content-Based Image Enhancement in the Compressed Domain Using Retinex Theory , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Firas Hassan,et al.  An FPGA-based architecture for a local tone-mapping operator , 2007, Journal of Real-Time Image Processing.

[23]  Antonios Gasteratos,et al.  Fast centre- surround contrast modification , 2008 .

[24]  Azeddine Beghdadi,et al.  Natural Enhancement of Color Image , 2010, EURASIP J. Image Video Process..

[25]  C. Chou,et al.  A novel simultaneous dynamic range compression and local contrast enhancement algorithm for digital video cameras , 2011, EURASIP J. Image Video Process..

[26]  E. Land Recent advances in retinex theory , 1986, Vision Research.

[27]  Geoffrey C. Fox,et al.  A VLSI Neural Network for Color Constancy , 1990, NIPS.