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学术报告

学术报告. 基于焦平面图像压缩的 CMOS 图像传感器研究. 于潇 2014.4.15. 目录. 概述. 设计思想. 整体结构仿真. 电路设计. 下一步工作. 一、概述. 近年来,随着 CMOS 图像传感器 (CIS) 分辨率和帧率的增加,数据传输的数据率在不断升高,使传输带宽成为了难以突破的瓶颈,因此图像压缩开始变得越来越重要。. 基于压缩感知的焦平面图像压缩方法将图像压缩的工作集成到 CIS 中,不对冗余数据进行输出,能从源头上消除数据冗余,提高处理效率。. 变换编码 预测编码 小波变换 [7][8][9][10][11][12][13][14].

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学术报告

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  1. 学术报告 基于焦平面图像压缩的CMOS图像传感器研究 于潇 2014.4.15

  2. 目录 概述 设计思想 整体结构仿真 电路设计 下一步工作

  3. 一、概述 近年来,随着CMOS图像传感器(CIS)分辨率和帧率的增加,数据传输的数据率在不断升高,使传输带宽成为了难以突破的瓶颈,因此图像压缩开始变得越来越重要。

  4. 基于压缩感知的焦平面图像压缩方法将图像压缩的工作集成到CIS中,不对冗余数据进行输出,能从源头上消除数据冗余,提高处理效率。基于压缩感知的焦平面图像压缩方法将图像压缩的工作集成到CIS中,不对冗余数据进行输出,能从源头上消除数据冗余,提高处理效率。

  5. 变换编码 预测编码 小波变换 [7][8][9][10][11][12][13][14] 限制:功耗,面积(电路复杂度) 综合各方面因素: 预测编码 改良后的Golomn-Rice编码 具体实现方法:MIXED-SIGNAL

  6. 优势: 图像压缩 去除相邻像素间的相关性 熵编码 提出一种 基于开关电容电路的预测方法 算法相对简单的编码方法 将以上两步操作同时集成在焦平面上 输出的Bit流无需再度处理可以直接进行传输或者存储,使处理效率大大提升

  7. 目录 设计思想 整体结构仿真 电路设计 下一步工作

  8. 二、设计思想 • 预测编码: • 帧间、帧内 • Residual= original value – predictor • have a peak distribution around zero

  9. 熵编码: 较短的码值 出现频率高的灰度值 较长的码值 出现频率低的灰度值 achieve code compression

  10. 1 2 3 4 2×2 Pixel unit

  11. Golomb-Rice Codes 13 K=1:q=6,r=1 111111 0 1 K=2:q=3,r=1 111 0 1 K=3:q=1,r=5 1 0 101

  12. 编码长度很大程度上取决于K值的选取 • 目前的硬件实现方法一般是对K取一个初始值,再对K值进行遍历,直到确定到合适的K值使编码长度最短,效率很低。[14] • 13的二进制形式: 1101 • K=3时 Golomn-Rice最佳编码:10101 • 如果结果为8bit,即00001101,那么编码结果是可以用其二进制形式的有效部分代替的。

  13. BIT PER PIXEL(BPP) OF PIXEL VALUE 0-50 5.6 5.76

  14. 目录 整体结构仿真 电路设计 下一步工作

  15. Compare the predictor and pixel value to generate sign bit s; • Generate residual value by subtractor: b7…b0 • initialize k=7; • If (bk=0) decrease k; • If (bk≠0) output bk…b0; • final codewords: the concatenationof s and bk…b0;

  16. bit per pixel, MSE, PSNR and compression ratio of lena, baboon and pepper.

  17. 目录 电路设计 下一步工作

  18. 目录 下一步工作

  19. 对编码的模块进行硬件实现 • 对Golomn-Rice熵编码模块进行硬件实现 • 对两种编码方法的压缩性能进行对比,验证行为级建模的结果

  20. 参考文献: • [1]Liang J Y, Chen C S, Huang C H, et al. Lossless compression of medical images using Hilbert space-filling curves. Computerized Medical Imaging and Graphics 2008; 32(3): 174-182. • [2]Chen Y T, Tseng D C. Wavelet-based medical image compression with adaptive prediction. Computerized medical Imaging and graphics, 2007; 31(1): 1-8. • [3] Lewiner T, Lopes H, Velho L, et al. Extraction and compression of hierarchical isocontours from image data. Computerized Medical Imaging and Graphics, 2006; 30(4): 231-242. • [4] Oike Y, El Gamal A. CMOS Image Sensor With Per-Column ΣΔ ADC and Programmable Compressed Sensing. IEEE Journal of Solid-State Circuits, 2013; 48(1): 318-328. • [5] Oliveira F D V R, Haas H L, Gomes J G R C, et al. CMOS imager with focal-plane analog image compression combining DPCM and VQ. IEEE Trans. Circuits and Systems I, 2013; 60(5):1331 – 1344. • [6] Kawahito S, Yoshida M, Sasaki M, et al. A CMOS image sensor with analog two-dimensional DCT-based compression circuits for one-chip cameras. IEEE Journal of Solid-State Circuits, 1997; 32(12): 2030-2041. • [7] Lin Z, Hoffman M W, Leon W D, et al. A CMOS front-end for a lossy image compression sensor. IEEE International Symposium on Circuits and Systems, 2007: 2838-2841. • [8] Zhang M, Bermak A. Quadrant-based online spatial and temporal compressive acquisition for CMOS image sensor. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2011; 19(9): 1525-1534. • [9] Olyaei A, Genov R. Focal-plane spatially oversampling CMOS image compression sensor. IEEE Transactions on Circuits and Systems I: Regular Papers, 2007; 54(1): 26-34. • [10] Nilchi A, Aziz J, Genov R. Focal-plane algorithmically-multiplying CMOS computational image sensor. IEEE Journal of Solid-State Circuits, 2009; 44(6): 1829-1839. • [11] Leon D, Balkir S, Sayood K, et al. A CMOS imager with pixel prediction for image compression. International Symposium on Circuits and Systems, 2003: IV-776-IV-779. • [12] Wang H T, Leon-Salas W D. A multiresolution algorithm for focal-plane compression. IEEE International Symposium on Circuits and Systems, 2012: 926-929. • [13] Lin Z, Hoffman M W, Schemm N, et al. A CMOS image sensor for multi-level focal plane image decomposition. IEEE Transactions on Circuits and Systems I, 2008; 55(9): 2561-2572. • [14] Leon-Salas W D, Balkir S, Sayood K, et al. A CMOS imager with focal plane compression using predictive coding. IEEE Journal of Solid-State Circuits, 2007; 42(11): 2555-2572. • [15] Yeh P S, Rice R F, Miller W H. On the optimality of a universal noiseless coder. Computing in Aerospace Conf, 1993: 490–498.

  21. Thank You !

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