一种基于人眼对比度敏感视觉特性的图像自适应量化方法

doi:10.11999/JEIT150848

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Abstract

In order to improve the compression ratio and quality of the image, combined with the contrast sensitivity characteristics of human vision system and the spectrum characteristics of image in the transform domain, a method is proposed to form the adaptive quantization table in image compression. And according to the JPEG coding algorithm and replacing the quantization table in JPEG, simulations are carried out for three images by programming, whose results are compared with JPEG compression at the same time. The results show that: compared with JPEG compression, under the same compression ratio, average SSIM and PSNR of three decompressed images increase by 1.67% and 4.96% after being compressed using adaptive quantization, respectively. They show that the adaptive quantization based on HVS is a good and practical method.

Key words： Image compression Quantization Human visual system characteristics Structural Similarity Index Measurement (SSIM)

Received: 16 July 2015 Published: 26 February 2016

PACS:	TN919.81
	O432

Fund:

The National Natural Science Foundation of China (61301237), The Scientific and Technological New-star Plan of Shaanxi Province, China (2015KJXX-42), The Specialized Research Foundation of Shaanxi Province Education Department, China (15JK1139)

Corresponding Authors: LIU Guizhong E-mail: liugz@xjtu.edu.cn

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Cite this article:

YAO Juncai,LIU Guizhong. An Adaptive Quantization Method of Image Based on the Contrast Sensitivity Characteristics of Human Visual System[J]. JEIT, 2016, 38(5): 1202-1210.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT150848 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I5/1202

[1]	BENOIT A, CAPLIER A, DURETTE B, et al. Using human visual system modeling for bio-inspired low level image processing[J]. Computer Vision & Image Understanding, 2010, 114(7): 758-773. doi: 10.1016/j.cviu.2010.01.011.
[2]	STAROSOLSKI R. New simple and efficient color space transformations for lossless image compression[J]. Journal of Visual Communication and Image Representation, 2014, 25(5): 1056-1063. doi: 10.1016/j.jvcir.2014.03.003.
[3]	OU Y F, XUE Y Y, and WANG Y. Q-STAR: a perceptual video quality model considering impact of spatial, temporal and amplitude resolution[J]. IEEE Transactions on Image Processing, 2014, 23(6): 2473-2486. doi: 10.1109/TIP.2014. 2303636.
[4]	DOUAK F, BENZID R, and BENOUDJIT N. Color image compression algorithm based on the DCT transform combined to an adaptive block scanning[J]. AEU International Journal of Electronics and Communications, 2011, 65(1): 16-26. doi: 10.1016/j.aeue.2010.03.003.
[5]	CHOU C H and LIU K C. Color image compression based on the measure of just noticeable color difference[J]. IET Image Processing, 2008, 2(6): 304-322. doi: 10.1049/iet-ipr: 20080034.
[6]	MULLEN K T. The contrast sensitivity of human color vision to red-green and blue-yellow chromatic gratings[J]. The Journal of Physiology, 1985, (359): 381-400. doi: 10.1113/jphysiol.1985.sp015591.
[7]	NADENAU M. Integration of human color vision models into high quality image compression[D]. [Ph.D. dissertation],École Polytechnique Fédérale de Lausanne, Switzerland, 2000: 69-112.
[8]	PELI E. Contrast sensitivity function and image discrimination[J]. Journal of the Optical Society of America A, 2001, 18(2): 283-293. doi: 10.1364/JOSAA.18.000283.
[9]	WATSON A B. Visual optimization of DCT quantization matrices for individual images[C]. Proceedings of 9th Computing in Aerospace Conference, San Diego, CA, U.S.A, 1993: 286-291. doi: 10.1109/DCC.1993.253132
[10]	JIMENEZ-RODRIGUEZ L, AULI-LLINAS F, and Marcellin M W. Visually lossless strategies to decode and transmit JPEG2000 imagery[J]. IEEE Signal Processing Letters, 2014, 21(1): 35-38. doi: 10.1109/LSP.2013.2290317.
[11]	GINESU G, MASSIDDA F, and GIUSTO D D. A multi- factors approach for image quality assessment based on a human visual system model[J]. Signal Processing: Image Communication, 2006, 21(4): 316-333. doi: 10.1016/j.image. 2005.11.005.
[12]	WANG X, JIANG G Y, ZHOU J M, et al. Visibility threshold of compressed stereoscopic image: effects of asymmetrical coding[J]. Journal of Imaging Science, 2013, 61(2): 172-182. doi: 10.1179/1743131X11Y.0000000035.
[13]	肖迪, 邓秘密, 张玉书. 基于压缩感知的鲁棒可分离的密文域水印算法[J]. 电子与信息学报, 2015, 37(5): 1248-1254. doi: 10.11999/JEIT141017.
	XIAO D, DENG M M, and ZHANG Y S. Robust and separable watermarking algorithm in encrypted image based on compressive sensing[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1248-1254. doi: 10. 11999/JEIT141017.
[14]	LU Z, LIN W, and YANG X, et al. Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation[J]. IEEE Transactions on Image Processing, 2005, 14(11): 1928-1942. doi: 10.1109/TIP.2005.　854478
[15]	吴倩, 张荣, 徐大卫. 基于稀疏表示的高光谱数据压缩算法[J]. 电子与信息学报, 2015, 37(1): 78-84. doi: 10.11999/ JEIT140214.
	WU Q, ZHANG R, and XU D W. Hyperspectral data compression based on sparse representation [J]. Journal of Electronics & Information Technology, 2015, 37(1): 78-84. doi: 10.11999/JEIT140214.
[16]	WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. doi: 10.1109/TIP.2003.819861.
[17]	姜求平, 邵枫, 蒋刚毅, 等. 基于视觉重要区域的立体图像视觉舒适度客观评价方法[J]. 电子与信息学报, 2014, 36(4): 875-881. doi: 10.3724/SP.J.1146.2013.00946.
	JIAN Q P, SHAO F, JIAN G Y, et al. An objective stereoscopic image visual comfort assessment metric based on visual important regions[J]. Journal of Electronics & Information Technology, 2014, 36(4): 875-881. doi: 10.3724/ SP.J.1146.2013.00946.