天津大学微电子学院,天津,300072
网络首发:2022-01-10,
纸质出版:2022
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郝亚喆, 张远, 张为. 二维离散小波变换低存储结构设计[J]. 西安交通大学学报, 2022,56(1):177-183.
Memory-Efficient Architecture of 2-D Discrete Wavelet Transform[J]. 2022, 56(1): 177-183.
郝亚喆, 张远, 张为. 二维离散小波变换低存储结构设计[J]. 西安交通大学学报, 2022,56(1):177-183. DOI: 10.7652/xjtuxb202201020.
Memory-Efficient Architecture of 2-D Discrete Wavelet Transform[J]. 2022, 56(1): 177-183. DOI: 10.7652/xjtuxb202201020.
针对二维9/7离散小波变换硬件架构中数据缓存需求高的问题
提出了一种基于提升算法的低存储架构。通过调整提升算法数据计算顺序
设计了一种动态计算二维小波变换的新型迭代分步计算方法。根据行、列变换的不同
对其分别做一维变换架构设计
其中行滤波器结构通过将输入数据进行三序列分裂
有效减少了寄存器数; 列滤波器结构通过单行输入处理消除转置存储器
同时实现了乘法器和加法器的复用。整体二维变换采用并行和流水线混合架构设计
关键路径延时减小到一个乘法器延迟。实验结果表明
对于1 024像素×1 024像素的图像
与其他提升结构相比
本结构片上内存使用减少了11.1%
硬件效率提高了8.2%以上; 与基于卷积的迭代计算方法相比
计算周期减少为现有结构的1/9。在型号为Xilinx Kintex7 XC7K325T的现场可编程逻辑门阵列上实现
吞吐率达到460 MB/s
且具有明显的硬件资源优势。
In the two-dimensional 9/7 discrete wavelet transform hardware architecture
high data storage requirements is always the problem to be solved. A lifting-based memory-efficient architecture is proposed. First
the data calculation sequence of the lifting algorithm is modified
and a new iterative step-by-step calculation method is designed to dynamically calculate the two-dimensional wavelet transform. According to the difference of row and column transforms
one-dimensional transform architecture design is performed on them. The row filter structure effectively reduces the number of registers by splitting the input data into three sequences. The column filter structure eliminates the data buffer of the transposed module through single-line input processing. At the same time
the multiplexing of multipliers and adders is realized. With parallel and pipeline hybrid architecture
the critical path delay of two-dimensional transform structure is reduced to a multiplier delay. Experimental results show that for an image of 1 024 pixel × 1 024 pixel
the on-chip memory requirement is reduced by 11.1%
and the hardware efficiency is raised by 8.24% in contrast to the existing lifting-based architectures. Compared with the iterative calculation method based on convolution
the calculation time is reduced to 1/9 of the existing structure. The proposed architecture is implemented on Xilinx FPGA Kintex7 XC7K325T
achieving a throughput of 460 MB/s and obvious hardware resource advantages.
王鑫, 高家明, 梁煜, 等. 一种高效存储多级二维9/7离散小波变换结构 [J]. 西安交通大学学报, 2018, 52(4): 111-116.
WANG Xin, GAO Jiaming, LIANG Yu, et al. A multi-level 2-D 9/7 DWT architecture with efficient memory [J]. Journal of Xi'an Jiaotong University, 2018, 52(4): 111-116.
IBRAHEEM M S, HACHICHA K, AHMED S Z, et al. High-throughput parallel DWT hardware architecture implemented on an FPGA-based platform [J]. Journal of Real-Time Image Processing, 2019, 16(6): 2043-2057.
黄绪, 梁煜, 张为, 等. 高性能的图像无损压缩知识产权核设计 [J]. 西安交通大学学报, 2020, 54(5): 102-108.
HUANG Xu, LIANG Yu, ZHANG Wei, et al. Design of intellectual property core for high-performance image lossless compression [J]. Journal of Xi'an Jiaotong University, 2020, 54(5): 102-108.
MOHANTY B K, MEHER P K. Memory-efficient high-speed convolution-based generic structure for multilevel 2-D DWT [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(2): 353-363.
CHAKRABORTY A, BANERJEE A. Area and memory efficient tunable VLSI implementation of DWT filters for image decomposition using distributed arithmetic [J]. International Journal of Electronics, 2020, 107(12): 1913-1939.
HEGDE G, REDDY K S, RAMESH T S. A new approach for 1-D and 2-D DWT architectures using LUT based lifting and flipping cell [J]. International Journal of Electronics and Communications, 2018, 97: 165-177.
DARJI A, AGRAWAL S, OZA A, et al. Dual-scan parallel flipping architecture for a lifting-based 2-D discrete wavelet transform [J]. IEEE Transactions on Circuits and Systems: II Express Briefs, 2014, 61(6): 433-437.
PINTO R, SHAMA K. An efficient architecture for modified lifting-based discrete wavelet transform [J]. Sensing and Imaging, 2020, 21(1): 53.
ZHANG Wei, WU Changkun, ZHANG Pan, et al. An internal folded hardware-efficient architecture for lifting-based multi-level 2-D 9/7 DWT [J]. Applied Sciences, 2019, 9(21): 4635.
HUANG C T, TSENG P C, CHEN L G. Generic RAM-based architectures for two-dimensional discrete wavelet transform with line-based method [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2005, 15(7): 910-920.
CHOUBEY A, MOHANTY B K. Novel data-access scheme and efficient parallel architecture for multi-level lifting 2-D DWT [J]. Circuits Systems and Signal Processing, 2018, 37(10): 4482-4503.
YE Linning, HOU Zujun. Memory efficient multilevel discrete wavelet transform schemes for JPEG2000 [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(11): 1773-1785.
BASIRI M A M, MAHAMMAD N S. An efficient VLSI architecture for lifting based 1D/2D discrete wavelet transform [J]. Microprocessors and Microsystems, 2016, 47: 404-418.
REIN S, REISSLEIN M. Performance evaluation of the fractional wavelet filter: a low-memory image wavelet transform for multimedia sensor networks [J]. Ad Hoc Networks, 2011, 9(4): 482-496.
TAUSIF M, JAIN A, KHAN E, et al. Low memory architectures of fractional wavelet filter for low-cost visual sensors and wearable devices [J]. IEEE Sensors Journal, 2020, 20(13): 6863-6871.
HUANG C T, TSENG P C, CHEN L G. Flipping structure: an efficient VLSI architecture for lifting-based discrete wavelet transform [J]. IEEE Transactions on Signal Processing, 2004, 52(4): 1080-1089.
TAUSIF M, KHAN E, HASAN M, et al. SFrWF: segmented fractional wavelet filter based DWT for low memory image coders [C]∥Proceedings of the 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics. Piscataway, NJ, USA: IEEE, 2017: 593-597.
TAUSIF M, KHAN E, HASAN M. BFrWF: block-based FrWF for coding of high-resolution images with memory-complexity constrained -devices [C]∥Proceedings of the 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering. Piscataway, NJ, USA: IEEE, 2018: 1-5.
董明岩, 雷杰, 王柯俨, 等. 高效低存储DWT的VLSI结构设计 [J]. 西安电子科技大学学报(自然科学版), 2016, 43(2): 35-40.
DONG Mingyan, LEI Jie, WANG Keyan, et al. Highly efficient VLSI architecture for DWT with low-storage implementation [J]. Journal of Xidian University(Natural Science), 2016, 43(2): 35-40.
WU Changkun, ZHANG Wei, JIA Qi, et al. Hardware efficient multiplier-less multi-level 2D DWT architecture without off-chip RAM [J]. IET Image Processing, 2017, 11(6): 362-369.
TODKAR S, SHASTRY P V S. Flipping based high performance pipelined VLSI architecture for 2-D discrete wavelet transform [C]∥Proceedings of the 2015 International Conference on Applied and Theoretical Computing and Communication Technology. Piscataway, NJ, USA: IEEE, 2015: 832-836.
DARJI A D, KUSHWAH S S, MERCHANT S N, et al. High-performance hardware architectures for multi-level lifting-based discrete wavelet transform [J]. EURASIP Journal on Image and Video Processing, 2014, 2014(1): 47.
PADMAVATI S, MESHRAM V, JAYADEVAPPA R. A hardware implementation of discrete wavelet transform for compression of a natural image [C]∥ Proceedings of the 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies. Piscataway, NJ, USA: IEEE, 2017: 1-5.
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