研究生: |
王尊賢 Wang, Tsun Hsien |
---|---|
論文名稱: |
基於虛擬多重曝光之高動態亮度範圍色調合成與色調映射之實現 Pseudo-Multiple-Exposures-Based High Dynamic Range Tone Fusion and Tone Mapping Realization |
指導教授: |
劉靖家
Liou, Jing Jia 邱瀞德 Chiu, Ching Te |
口試委員: |
張振豪
Chang, Chen Hao 黃穎聰 Huang, Yin Tsung 黃錫瑜 Huang, Shi Yi 賴尚宏 Lai, Shang Hong 楊家輝 Yang, Jia Ferr |
學位類別: |
博士 Doctor |
系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 英文 |
論文頁數: | 140 |
中文關鍵詞: | 虛擬多重曝光 、高動態亮度解析範圍 、色調映射 、反向色調映射 、多核心系統單晶片 |
外文關鍵詞: | pseudo multiple exposures, High dynamic range (HDR), Tone mapping, Inverse tone mapping, Multi-core System on Chip (SoC) |
相關次數: | 點閱:2 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著顯示技術的進步,新一代的顯示裝置相較於傳統顯示裝置而言,已能夠有效改善亮度的動態顯示範圍。利用逆色調映射 (Inverse Tone Mapping) 的方法,可將低動態亮度範圍的影像,轉換至高動態亮度範圍 (High Dynamic Range , HDR)。這方法當中,一些方法需要利用相同場景的不同曝光影像,才能將低動態亮度範圍的影像轉換至 高動態亮度範圍,然而對於大多數影像或是視訊而言,只能取得單一曝光的影像或視訊。因此在本篇論文中,我們提出虛擬多重曝光影像以及區域對比增強的方法,產生高動態亮度範圍的影像。首先,我們提出利用能夠改變不同曝光值的S-曲線,將一張低動態亮度範圍的影像,產生多張虛擬曝光的影像。由於在虛擬曝光影像中的某些區域包含了一些,能夠被觀察的影像細節,因此我們利用區域對比增強的方法,針對在不同曝光影像中的的某些區域增強其對比,進而利用色調合成的方法產生高動態亮度範圍的影像。在我們的方法中,我們針對兩種類型的區域做區域對比增強,分別為最暗影像的最亮區域,以及最亮影像的最暗區域。在區域對比增強後所得到的高動態亮度範圍的影像,和其他逆色調映射的方法做比較,可以發現我們的方法在錯誤對比的量測中,對比的錯誤總數是比較低的。
此外,在這篇論文中,我們將我們的方法在不同的多處理器的環境中,分析其運算效能並加以實現。經由分析之後發現,我們的演算法當中,產生五張不同曝光的影像,以及計算不同的權重值有最高的運算量,因此我們將這兩部分的運算放在不同處理器中平行處理。經由實驗發現,我們將產生五張不同曝光的影像,以及計算不同的權重值平行化處理之後,指令數目有效的降低,在六個處理器平行處理的環境中,運算時間可減少大約70%。經由實驗證實,我們所提出的方法,很容易在多處理器的平台上實現,並能有效提高運算效能。
為了能將高動態亮度範圍的影像或視訊,顯示在傳統低動態亮度範圍的液晶顯示裝置上,所採用的方法一般稱為色調映射。過去數十年期間,許多色調映射 (Tone Mapping)的方法,陸續被提出。一個理想的色調映射處理器,應該要包含一顆功能較為強健的處理器,同時應具備面積小,及低功耗的特性,以提高處理色調映射的彈性。因此我們選擇安謀 ARM 處理器為主的系統晶片平台,同時在此系統晶片平台上,開發色調映射應用的特殊應用積體電路晶片。在本篇論文中,我們經由系統最佳化的分析,開發具有硬體與軟體最佳化的色調映射系統晶片。我們整合了全影像色調映射與以局部影像為處理單元的區域影像色調映射,同時我們經由系統分析,開發最佳的軟硬體架構。在我們的最佳化的開發過程中,有四個主要的步驟分別是 :定義功能性模組,運算功能的強化,硬體與軟體功能模組的區分,以及成本函數的分析。根據我們所提出的架構,我們成功的開發了一個具有整合全影像色調映射與區域影像色調映射的色調映射系統晶片,針對不同的應用可以選擇合適的色調映射的方法。我們所開發的系統晶片使用台積電0.13微米的製程,時脈頻率可達100MHz,面積為8.1mm^2,可處理影像大小為 1024X768,且每秒可處理60張影像。經由軟硬體最佳化後,硬體面積可以有效降低達50%。
New generations of display technologies provide a significantly improved dynamic range compared to conventional display devices. Inverse tone mapping methods have been proposed to convert low dynamic range (LDR) images to high dynamic range (HDR) ones, and several of them require multiple-exposures LDR images of the same scene as inputs. However, the vast majority of LDR images and videos available have only one single exposure. In this dissertation, we propose a region-based enhancement of the pseudo exposures to generate an HDR image. First, we present an exposure dependent $S$ curve to convert one
LDR image to the pseudo-multiple-exposures images.
Only certain regions of the pseudo-multiple-exposures images contain noticeable detailed information. We propose a region-based enhancement on the pseudo-multiple-exposures images to boost details in the most distinct region. Thereby the region-enhanced pseudo-multiple-exposures images are fused into an HDR image. The fused image thus enhances details in the brightest region of the darkest image and the darkest region of the brightest image. Compared with other inverse tone mapped methods, our method generates the lower total contrast error which is measured under the dynamic range independent image quality assessment method.
In this dissertation, we analyze the performance of implementing inverse tone mapping operation in various multi-core environments. We parallelize the two procedures to produce five HDR images and different weighting values. The ratio of the instruction counts for producing five HDR images and weighting values are both reduced, and the total simulation time decreases around 70% under six-core environment. The reduction is because these functions are implemented parallel with multi-thread on a multi-core platform. The analysis of hardware performance shows that our proposed method is easy to be realized in a multi-core system to speed up the performance.
The technology to display HDR images or videos on conventional LCD devices is called tone mapping which has the different algorithms that have been developed in the past decade. An ideal HDR tone mapping processor should have some characteristics such as a robust core functionality, high flexibility, and low power consumption. Therefore, an $ARM^{TM}$-core-based System-on-Chip (SoC) platform with an HDR tone mapping application-specific integrated circuit (ASIC) is suitable for such applications. In this dissertation, we present a systematic methodology for the development of a tone mapping processor of optimized architecture using an ARM SoC platform, and illustrate the use of this novel HDR tone mapping processor for both photographic and gradient compression. Optimization is achieved through four major steps: common module extraction, computation of power enhancement, hardware/software partition, and cost function analysis. Based on our proposed scheme, we present an integrated photographic and gradient tone-mapping processor that can be configured for various applications. This newly-developed processor can process the image size 1024X768 at 60 frame per second, runs at 100 MHz and consumes a core area of 8.1 mm^2 under TSMC 0.13 um technology, resulting in a 50% improvement in speed and area as compared with previously described processors.
Bibliography
[1] B. A. Wandell, Foundations of Vision. Stanford University, 1995.
[2] E. Reinhard, T. Kunkel, Y. Marion, J. Brouillat, R. Cozot, and K. Bouatouch,
“Image display algorithms for high and low dynamic range display devices,”
Journal of the Society for Information Display, vol. 15, no. 12, pp. 997–1014,
December 2007.
[3] E. Reinhard, G. Ward, S. N. Pattanaik, P. E. Debevec, and W. Heidrich, High
Dynamic Range Imaging - Acquisition, Display, and Image-Based Lighting (2.
ed.). Academic Press, 2010.
[4] F. Banterle, A. Artusi, K. Debattista, and A. Chalmers, Advanced High Dynamic
Range Imaging: Theory and Practice. Natick, MA, USA: AK Peters
(CRC Press), 2011.
[5] H. Seetzen, L. Whitehead, and G. Ward, “A high dynamic range display
using low and high resolution modulators,” In Society for Information Display
Internatiational Symposium Digest, Tech. Rep., 2003.
[6] H. Seetzen, H. Li, L. Ye, W. Heidrich, L. Whitehead, and G. Ward, “25.3:
Observations of luminance, contrast and amplitude resolution of displays,” In
Society for Information Display (SID) Digest, pp. 1229–1233, 2006.
[7] S. Mann, R. W. Picard, S. Mann, and R. P. Being, “Undigital’ with digital
cameras: Extending dynamic range by combining differently exposed pictures,”
in Proc. IST’s 48th Annual Conference, 5 1995, pp. 442–448.
126
[8] P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps
from photographs,” in Proc. SIGGRAPH, 1997, pp. 369–378.
[9] S. Nayar and T. Mitsunaga, “High dynamic range imaging: Spatially varying
pixel exposures,” in IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, vol. 1, 2000, pp. 472–479.
[10] R. Mantiuk, G. Krawczyk, K. Myszkowski, and H. P. Seidel, “Perceptionmotivated
high dynamic range video encoding,” ACM Transactions on Graphics,
vol. 23, no. 3, pp. 733–741, 2004.
[11] K. Saito, “Electronic image pickup device,” Japanese Patent 08-340486, 12
1996.
[12] E. Ikeda, “Image data processing apparatus for processing combined image
signals in order to extend dynamic range,” U.S. Patent 5801773, 9 1998.
[13] R. A. Street, “High dynamic range segmented pixel sensor array,” U.S. Patent
5789737, 8 1998.
[14] http://www.spheron.com, Oct. 2012.
[15] M. Schober, J. Keinert, M. Ziegler, J. Seiler, M. Niehaus, G. Schuller,
A. Kaup, and S. Foessel, “Evaluation of a high dynamic range video camera
with non-regular sensor,” in Proc. of SPIE Electronic Imaging, 2013, pp.
86 600M–1–86 600M–12.
[16] F. Banterle, M. Dellepiane, and R. Scopigno, “Enhancement of low dynamic
range videos using high dynamic range backgrounds,” in EUROGRAPHICS,
2011.
127
[17] A. Chalmers, G. Bonnet, F. Banterle, P. Dubla, K. Debattista, A. Artusi,
and C. Moir, “A high-dynamic-range video solution,” in The 2nd ACM SIGGRAPH
Conference and Exhibition in Asia, 2009, pp. 71–72.
[18] P. Didyk, R. Mantiuk, M. Hein, and H.-P. Seidel, “Enhancement of bright
video features for hdr displays,” Computer Graphics Forum (Proceedings Eurographics
Symposium on Rendering 2008, Sarajevo, Bosnia and Herzegovina),
vol. 27, no. 4, pp. 1265–1274, 2008.
[19] T. Aydin, R. Mantiuk, K. Myszkowski, and H. Seidel, “Dynamic range independent
image quality assessment,” in SIGGRAPH, 2008, pp. 1–10.
[20] A. G. Rempel, M. Trentacoste, H. Seetzen, and H. D. Young, “Ldr2hdr:
On-the-fly reverse tone mapping of legacy video and photographs,” ACM
Transactions on Graph, vol. 26, no. 3, pp. 39.1–39.6, 2007.
[21] L. Meylan, S. Daly, and S. S¨usstrunk, “The Reproduction of Specular
Highlights on High Dynamic Range Displays,” in IS&T/SID 14th Color
Imaging Conference (CIC), 2006.
[22] S. Daly and X. Feng, “Bit depth extension using spatiotemporal microdither
based on models of theequivalent input noise of the visual system,” in Proceedings
of Color Imaging VIII: Processing, Hardcopy, and Applications. Bellingham,
WA, USA: SPIE, 2003, pp. 455–466.
[23] D. Scoot and F. Xiaofan, “Decontouring: Prevention and removal of false
contour artifacts,” in Proceeding of Human Vision. SPIE, 2004, pp. 130–
149.
128
[24] L. Meylan, S. Daly, and S. Susstrunk, “Tone mapping for high dynamic range
displays,” in Proc. IS&T/SPIE Electronic Imaging: Human Vision and Electronic
Imaging XII. SPIE, 2007.
[25] F. Banterle, P. Ledda, K. Debattista, and A. Chalmers, “Inverse tone mapping,”
in Proc. of GRAPHITE ’06, 2006, pp. 349–356.
[26] L. Wang, L.-Y. Wei, K. Zhou, B. Guo, and H.-Y. Shum, “High dynamic
range image hallucination,” in ACM SIGGRAPH 2007 sketches, ser.
SIGGRAPH ’07. New York, NY, USA: ACM, 2007. [Online]. Available:
http://doi.acm.org/10.1145/1278780.1278867
[27] F. Banterle, P. Ledda, K. Debattista, and A. Chalmers, “Expanding
low dynamic range videos for high dynamic range applications,” in
Proceedings of the 24th Spring Conference on Computer Graphics, ser. SCCG
’08. New York, NY, USA: ACM, 2010, pp. 33–41. [Online]. Available:
http://doi.acm.org/10.1145/1921264.1921275
[28] B. Masia, S. Agustin, R. W. Fleming, O. Sorkine, and D. Gutierrez, “Evaluation
of reverse tone mapping through varying exposure conditions,” ACM
Transactions on Graphics (Proc. of SIGGRAPH Asia), vol. 28, no. 5, pp.
160:1–160:8, 2009.
[29] H. Landis, “Production-ready global illumination,” in SIGGRAPH Course
Notes 16, 2002, pp. 87–101.
[30] F. Banterle, P. Ledda, K. Debattista, A. Chalmers, and M. Bloj, “A framework
for inverse tone mapping,” The Visual Computer: International Journal
of Computer Graphics, vol. 23, pp. 467–478, 2007.
129
[31] R. Kovaleski and M. M. Oliveira, “High-quality brightness enhancement functions
for real-time reverse tone mapping,” The Visual Computer, vol. 25, pp.
539–547, 2009.
[32] Y. Huo, F. Yang, and V. Brost, “An inverse tone mapping method for displaying
legacy images on hdr monitor,” in Proceedings of the 9th International
Symposium on Linear Drives for Industry Applications, 2014, pp. 733–741.
[33] R. Mantiuk, K. J. Kim, A. G. Rempel, and W. Heidrich, “Hdr-vdp-2:
a calibrated visual metric for visibility and quality predictions in all
luminance conditions,” in ACM SIGGRAPH 2011 papers, ser. SIGGRAPH
’11. New York, NY, USA: ACM, 2011, pp. 40:1–40:14. [Online]. Available:
http://doi.acm.org/10.1145/1964921.1964935
[34] R. Mantiuk, S. Daly, K. Myszkowski, and H. P. Seidel, “Predicting visible
differences in high dynamic range images - model and its calibration,” in Human
Vision and Electronic Imaging X, IS&T/SPIE’s 17th Annual Symposium
on Electronic Imaging, 2005, pp. 204–214.
[35] F. Banterle, P. Ledda, K. Debattista, M. Bloj, A. Artusi, and A. Chalmers, “A
psychophysical evaluation of inverse tone mapping techniques,” Computer
Graphics Forum, vol. 28, no. 1, pp. 13–25, 2009. [Online]. Available:
http://dx.doi.org/10.1111/j.1467-8659.2008.01176.x
[36] Y. Hoskote, S. Vangal, A. Singh, N. Borkar, and S. Borkar, “A 5-ghz mesh
interconnect for a teraflops processor,” IEEE Micro, vol. 27, pp. 51–61, 2007.
130
[37] J. Howard, S. Dighe, Y. Hoskote, and S. Vangal, “A 48-core ia-32 messagepassing
processor with dvfs in 45 nm cmos,” in Proc. Intl. Solid-State Circuits
Conf., february 2010.
[38] E. Painkras, L. A. Plana, J. Garside, S. Temple, and F. Galluppi, “”spinaker:
A 1-w 18-core system-on-chip for massively-parallel neural network simulation”,”
IEEE Journal of Solid State Circuits, vol. 48, no. 8, pp. 1943–1953,
Aug 2013.
[39] J. Tumblin and H. E. Rushmeier, “Tone reproduction for realistic images,”
IEEE Computer Graphics and Applications, vol. 13, no. 6, p. 428, 1993.
[40] K. Chiu, M. Herf, P. Shirley, S. Swamy, C. Wang, and K. Zimmerman, “Spatially
nonuniform scaling functions for high contrast images.” in In Graphics
Interface, 1993, pp. 244–245.
[41] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction
for digital images,” ACM Trans. Graph. (Proc. of ACM SIGGRAPH),
vol. 21, no. 3, pp. 267–276, 2002.
[42] J. A. F. S. N. Pattanaik, M. D. Fairchild, and D. P. Greenberg, “A multiscale
model of adaptation and spatial vision for image display,” in In Proceedings
of SIG-GRAPH 98, 1998, pp. 287–298.
[43] R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic
range compression.” ACM Transactions on Graphics, vol. 21, no. 3, pp. 249–
256, 2002.
131
[44] M. D. Grossberg and S. K. Nayar, “Determining the camera response from
images: What is knowable ?” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 25, no. 11, pp. 1455–1467, 2003.
[45] S. B. Kang, M. Uyttendaele, S. Winder, and R. Szeliski, “High dynamic range
video,” ACM Transactions on Graphics, vol. 22, no. 3, pp. 319–325, 2003.
[46] T. Mertens, J. Kautz, and F. VanReeth, “Exposure fusion: A simple and
practical alternative to high dynamic range photography,” Computer Graphics
Forum, vol. 28, no. 1, pp. 161–171, 2009.
[47] G. Ward, “A contrast-based scalefactor for luminance display,” 1994.
[48] M. Ashikhmin, “A tone mapping algorithm for high contrast images,” in
Proceeding of 13th Eurographics Workshop on Rendering, 2002, pp. 145–155.
[49] F. Drago, K. Myszkowski, T. Annen, and N. Chiba, “Adaptive logarithmic
mapping for displaying high contrast scenes,” in EUROGRAPHICS, 2003.
[50] A. O. Aky¨uz, R. Fleming, B. E. Riecke, E. Reinhard, and H. H. B¨ulthoff,
“Do hdr displays support ldr content: a psychophysical evaluation,”
ACM Trans. Graph., vol. 26, no. 3, Jul. 2007. [Online]. Available:
http://doi.acm.org/10.1145/1276377.1276425
[51] F. Banterle, A. Chalmers, and R. Scopigno, “Real-time high fidelity inverse
tone mapping for low dynamic range content,” in Miguel A, Otaduy OS,
editors. Eurographics 2013 short papers. Eurographics, 2013, pp. 41–44.
[52] P. E. Debevec, “A median cut algorithm for light probe sampling,” in ACM
SIGGRAPH 2005 poster, 2005.
132
[53] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York,
NY, USA: Wiley Interscience, 2001.
[54] K. Perlin and E. M. Hoffert, “Hypertexture,” in Computer Graphics ,ACM,
vol. 23, 1989, pp. 253–262.
[55] F. J. Blommaert and J. B. Martens, “An object-oriented model for brightness
perception,” Spatial Vision, vol. 5, no. 1, pp. 15–41, 1990.
[56] F. Durand and J. Dorsey, “Fast bilateral filtering for the display of highdynamic-
range images,” in SIGGRAPH, vol. 31, 2002, pp. 53–58.
[57] J. Tumblin and G. Turk, “A boundary hierarchy for detail-preserving contrast
reduction,” in SIGGRAPH 99, 1999, pp. 83–90.
[58] J. M. DicarloI and B. A. Wandell, “Rendering high dynamic range images,”
in Proceedings of the SPIE : Image Sensors, vol. 3965, 2001, pp. 392–401.
[59] K. K. Biswas and S. N. Pattanaik, “A simple spatial tone mapping operator
for high dynamic range images operator for high dynamic range images,” in
Proc. IS&T/SID 13th Color Imaging Conference (CIC), 2005.
[60] G. Messina, A. Catorina, S. Battiato, and A. Bosco, “Image quality improvement
by adaptive exposure correction techniques,” in IEEE ICME 2003, 2003,
pp. 549–559.
[61] M. A. Robertson, S. Borman, and R. L. Stevenson, “Dynamic range improvement
through multiple exposures,” in IEEE ICIP 1999, vol. 3, 10 1999, pp.
159–163.
133
[62] J. W. Lee, R.-H. Park, and S. Chang, “Local tone mapping using the
k-means algorithm and automatic gamma setting,” Consumer Electronics,
IEEE Transactions on, vol. 57, no. 1, pp. 209 –217, february 2011.
[63] T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion,” in Proceedings
of the 15th Pacific Conference on Computer Graphics and Applications, 2007,
pp. 382–390.
[64] http:// www.hdrone.com/ 2013/01/ exposure-fusion-in-photomatix-for-ultranatural-
photos/, May 2014.
[65] T. H. Wang, C. W. Chiu, W. C. Wu, J. W. Wang, C. Y. Lin, C. T. Chiu,
and J. J. Liou, “Pseudo-multiple-exposure-based tone fusion with local region
adjustment,” IEEE Transactions on Multimedia, vol. 17, no. 4, pp. 470–484,
2015.
[66] D. R. Kincaid, “Celebrating fifty years of david m. young’s successive overrelaxation
iterative method. in m. feistauer, v. dolejsi, p. knobloch, k. najzar
(eds.),” in Numerical Mathematics and Advanced Applications. New York:
Springer, 2004, pp. 549–558.
[67] S. N. Wansundara, “Solving the discrete poisson equation using the fast
fourier transform technique.” Memorial University of Newfoundland, St.
John’s., Tech. Rep., 2002.
[68] T. H. Wang, W. M. Ke, D. C. Zwao, F. C. Chen, and C. T. Chiu, “Design and
implementation of a real-time global tone mapping processor for high dynamic
range video.” in IEEE International Conference on Image Proecssing 2007,
2007, pp. 209–212.
134
[69] V. Kantabutra, “On hardware for computing exponential and trigonometric
functions,” IEEE Transactions on Computers, vol. 45, no. 3, pp. 328–339,
1996.
[70] J. C. Yao and C. Hsu, “New approach for fast sine transform.” Electronics
Letters, vol. 28, no. 15, pp. 1398–1399, 1996.
[71] O. Chika, G. Stanislav, and Y. P. Orly, “Hardware implementation of automatic
rendering tone mapping algorithm for a wide dynamic range display,”
Journal of Low Power Electronics and Applications, vol. 3, pp. 337–367, 2013.
[72] F. Hassan and J. Carletta, “An fpga-based architecture for a local tone mapping
operator,” Journal of Real-Time Image Process, vol. 2, pp. 293–308,
2007.
[73] J. Carletta and F. Hassan, “Method for real-time implementable local tone
mapping for high dynamic range images,” US Patent 20090041376, 2 2009.
[74] L. Vytla, F. Hassan, and J. Carletta, “A real-time implementation of gradient
domain high dynamic range compression using a local poisson solver,” Journal
of Real-Time Image Process, vol. 8, p. 153:167, 2013.
[75] R. U. na, P. M.-C. nada, J. M. G´omez-L´opez, C. A. Moillas, and F. J. Pelayo,
“Real-time tone mapping on gpu and fpga,” EURASIP Journal of Image and
Video Processing, 2012.
[76] O. Chika, G. Stanislav, and Y. P. Orly, “An in-depth analysis and image
quality assessment of and exponent-basesd tone mapping algorithm,” Inf.
Models Anal., pp. 236–250, 2012.