一種基于人眼對(duì)比度敏感視覺特性的圖像自適應(yīng)量化方法
doi: 10.11999/JEIT150848
-
2.
(西安交通大學(xué)電子與信息工程學(xué)院 西安 710049) ②(陜西理工學(xué)院物理與電信工程學(xué)院 漢中 723000)
國家自然科學(xué)基金(61301237),陜西省青年科技新星計(jì)劃(2015KJXX-42),陜西省教育廳專項(xiàng)科研基金(15JK1139)
An Adaptive Quantization Method of Image Based on the Contrast Sensitivity Characteristics of Human Visual System
-
2.
(The School of Electronic and Information Engineering, Xi&rsquo
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)
-
摘要: 為了提高圖像的壓縮比和壓縮質(zhì)量,結(jié)合人眼對(duì)比度敏感視覺特性和圖像變換域頻譜特征,該文提出一種自適應(yīng)量化表的構(gòu)建方法。并將該表代替JPEG中的量化表,且按照J(rèn)PEG的編碼算法對(duì)3幅不同的彩色圖像進(jìn)行了壓縮仿真實(shí)驗(yàn)驗(yàn)證,同時(shí)與JPEG壓縮作對(duì)比分析。實(shí)驗(yàn)結(jié)果表明:與JPEG壓縮方法相比,在相同的壓縮比下,采用自適應(yīng)量化壓縮后,3幅解壓彩色圖像的SSIM和PSNR值分別平均提高了1.67%和4.96%。表明該文提出的結(jié)合人眼視覺特性的自適應(yīng)量化是一種較好的、有實(shí)用價(jià)值的量化方法。
-
關(guān)鍵詞:
- 圖像壓縮 /
- 量化 /
- 人眼視覺特性 /
- 結(jié)構(gòu)相似度
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. -
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. 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. 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. 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. 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. 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. NADENAU M. Integration of human color vision models into high quality image compression[D]. [Ph.D. dissertation],cole Polytechnique Fdrale de Lausanne, Switzerland, 2000: 69-112. 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. 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 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. 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. 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. 肖迪, 鄧秘密, 張玉書. 基于壓縮感知的魯棒可分離的密文域水印算法[J]. 電子與信息學(xué)報(bào), 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. 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 吳倩, 張榮, 徐大衛(wèi). 基于稀疏表示的高光譜數(shù)據(jù)壓縮算法[J]. 電子與信息學(xué)報(bào), 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. 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. 姜求平, 邵楓, 蔣剛毅, 等. 基于視覺重要區(qū)域的立體圖像視覺舒適度客觀評(píng)價(jià)方法[J]. 電子與信息學(xué)報(bào), 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. -
計(jì)量
- 文章訪問數(shù): 1810
- HTML全文瀏覽量: 150
- PDF下載量: 601
- 被引次數(shù): 0