一种自适应图像插值算法及加速引擎的协同设计

严忻恺; 丁晟

doi:10.11999/JEIT221503

一种自适应图像插值算法及加速引擎的协同设计

doi: 10.11999/JEIT221503

严忻恺^{1, 2, ,},
丁晟²

1.
浙江大学杭州 310015
2.
江苏省专用集成电路设计重点实验室(无锡) 无锡 214153

基金项目: 江苏省高等学校自然科学研究项目(19KJB510027)，江苏省“333工程”科研资助项目(BRA2020318)，江苏省专用集成电路设计重点实验室开放基金(2020KLOP005)

详细信息

作者简介:
严忻恺：男，讲师，博士生，研究方向为智能图形芯片设计等

丁晟：男，副教授，博士，研究方向FPGA设计等

通讯作者:
严忻恺　yanxinkai@zju.edu.cn

中图分类号: TN492
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- PDF下载量: 98
- 被引次数: 0
出版历程
- 收稿日期: 2022-12-02
- 修回日期: 2023-04-12
- 网络出版日期: 2023-04-19
- 刊出日期: 2023-09-27

Adaptive Image Interpolation Algorithm and Acceleration Engine Co-Design

YAN Xinkai^{1, 2
, ,},
DING Sheng²

1.
Zhejiang University, Hangzhou 310015, China
2.
Jiangsu Key Laboratory Of Asic Design (Wuxi), Wuxi 214153, China

Funds: The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (19KJB510027), Jiangsu “333” Scientific Research Project (BRA2020318), The Development Fundation of Jiangsu Key Laboratory of Asic Design (2020KLOP005)

摘要

摘要: 为提高高清彩色图像超分辨率重建效果，该文提出了一种基于边缘对比度的新型自适应图像插值算法。使用边缘对比度检测和不同尺度的感受野来自适应选择Lanczos插值的系数，自适应性和不同感受野可以进一步提升图像放大质量，图像质量相比于双线性插值平均峰值信噪比(PSNR)提高1.1 dB，结构相似度(SSIM)提高0.025，图像感知相似度(LPIPS)提高0.051，相比于双三次插值平均PSNR提高0.34 dB，SSIM提高0.01，LPIPS提高0.033。同时为减少硬件资源以及提高存储效率协同设计了一种高并行、高能效的加速插值引擎架构，通过两级数据重用和系数脉动机制极大提高计算访存比。加速引擎在16 nm工艺库的综合结果达到2 GHz时钟频率；在Xilinx Zynq Ultra scale+ xczu15eg FPGA上工作频率达到200 MHz，帧速度(fps)达到60的实时性能。
- 插值算法 /
- 自适应 /
- 并行度 /
- 高能效 /
- 加速引擎
Abstract: In order to improve the super-resolution reconstruction effect of the high-definition color image, a new adaptive image interpolation algorithm based on edge contrast is proposed, which chooses adaptively the coefficients of Lanczos interpolation by edge contrast detection and receptive fields with different scales. Adaptability and diverse receptive fields can further improve the quality of image magnification. Compared with the bilinear interpolation algorithm, the Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) are improved by 1.1 dB, 0.025, 0.051, respectively. Compared with the bicubic interpolation algorithm, the PSNR, SSIM and LPIPS are improved by 0.34 dB, 0.01, 0.033, respectively. Moreover, in order to reduce the hardware resources and improve the storage efficiency, a high parallelized and high efficiency accelerated architecture is proposed. A 2-level data reuse and coefficients pulsation mechanism are employed to improve the computation-memory access ratio greatly. The synthesis result of the acceleration engine in the 16nm process library can reach the 2 GHz clock frequency. The operating frequency of FPGA project deployed in Xilinx Zynq Ultra scale+ xczu15eg can reach up to 200 MHz as well, which means that the algorithm can adapt to the frame rate (fps) up to 60.
- Interpolation algorithm /
- Adaptive /
- Parallelism /
- Energy efficiency /
- Acceleration engine

HTML全文

图 1 基于边缘优化的图像插值算法流程图

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图 2 各级感受野像素的合并插值示意图

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图 3 加速引擎总体架构示意图

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图 4 插值引擎总体结构框图

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图 5 边缘检测单元结构图

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图 6 插值计算单元结构图

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表 1 乘加器单元数目

模块名	数量	备注
水平插值计算单元	8×3	int8×int16+int24/ int16×int16+int32
竖直插值计算单元	1×3	int8×int16+int24/ int16×int16+int32

下载: 导出CSV

表 2 加法器单元数目

模块名	数量	位宽
阈值计算单元	24	Int8
梯度计算单元	4	Int8
	8	Int9
	4+4(绝对值)	Int10
	2	Int11
边缘处理单元	4	Int12
近似灰度转换	21	Int8

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表 3 插值引擎的RAM容量表

模块名	数量	容量
插值系数	4	8×4×2B
共计		256B

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表 4 插值引擎的寄存器数目表

模块名	数量	位宽	总数
像素寄存器阵列	88	3B	264 Byte
近似灰度阵列	42	1B	42 Byte
乘累加结果缓存	51	4B+2B	306 Byte
控制+边缘检测			80 Byte
共计			692 Byte

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表 5 不同算法的复杂度对比

算法	时间复杂度	乘法次数	像素点数量
双线性插值	O(n)	6	4
双三次插值	O(n)	20	16
Lanczos3插值	O(n)	42	36
Lanczos4插值	O(n)	72	64
本文算法	O(n)	72	64

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表 6 不同算法的PSNR对比(dB)

算法	平均PSNR	最佳PSNR	最差PSNR
双线性插值	30.88	35.08	23.15
双三次插值	31.59	36.00	23.66
Lanczos3插值	31.82	36.34	23.82
Lanczos4插值	31.84	36.39	23.83
本文算法	31.93	36.42	23.94

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表 7 不同算法的SSIM对比

算法	平均SSIM	最佳SSIM	最差SSIM
双线性插值	0.849	0.920	0.599
双三次插值	0.864	0.936	0.625
Lanczos3插值	0.868	0.942	0.631
Lanczos4插值	0.869	0.944	0.632
本文算法	0.874	0.947	0.650

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表 8 不同算法的LPIPS对比

算法	平均LPIPS	最佳LPIPS	最差LPIPS
双线性插值	0.291	0.133	0.517
双三次插值	0.273	0.104	0.513
Lanczos3插值	0.276	0.098	0.523
Lanczos4插值	0.275	0.096	0.520
本文算法	0.244	0.079	0.479

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表 9 FPGA硬件实现的指标对比

参数名称	文献^[19]	文献^[25]	本文
图像大小	256×256灰度	256×256灰度	960×540彩色
插值算法	BICUBIC	NEDI	本文算法
FPGA平台	Artix-7	virtex-7	Xilinx Zynq
频率(MHz)	289.2	100	200
Slice LUTs	359	4883	19038
Slice Reg	162	2705	6492
DSPs	0	42	27
*说明：FPGA资源为单个插值引擎

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表 10 ASIC硬件实现的指标对比

硬件指标	VLSI’18^[26]	ISSCC’21^[22]	本文
工艺	65 nm	40 nm	16 nm
算法	CNN	插值+预学习	插值
吞吐量(fps)	60	90	60*
数据精度	INT8/INT16	INT8/INT16	INT8/INT16
频率(MHz)	200	200	2000
SRAM(KB)	572	371	2.23
门数量(M)	–	3.11	0.23
*说明：本文实例化4个插值引擎实现的吞吐量(fps)为60

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