微信服务号

微信订阅号

2025年5月1日 6:38 星期四
首页 > 过刊浏览>2018年第29卷第4期 >973-986. DOI:10.13328/j.cnki.jos.005410
PDF HTML阅读 XML下载 导出引用 引用提醒
无参考屏幕内容图像质量评价
作者:
作者简介:

朱映映(1976-),女,山东莒南人,博士,研究员,主要研究领域为多媒体信息处理,多媒体信息安全;王旭(1986-),男,博士,研究员,CCF专业会员,主要研究领域为视频编码压缩,图像,视频质量评价;曹磊(1989-),男,硕士,主要研究领域为图像质量评价.

通讯作者:

王旭,E-mail:wangxu@szu.edu.cn

基金项目:

国家自然科学基金（61602314，61602312，61501299，61672443）；广东省自然科学基金（2016A030313043，2016A030310058）；深圳市科技计划基础研究项目（JCYJ20160331114551175，JCYJ20150324141711630，JCYJ20130326105637578）


No Reference Screen Content Image Quality Assessment
Author:
Fund Project:

National Natural Science Foundation of China (61602314, 61602312, 61501299, 61672443); Natural Science Foundation of Guangdong Province of China (2016A030313043, 2016A030310058); Fundamental Research Project in the Science and Technology Plan of Shenzhen (JCYJ2016 0331114551175; JCYJ20150324141711630; JCYJ20130326105637578)

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [38]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    随着多客户端交互多媒体应用的快速发展，屏幕内容图像（screen content image，简称SCI）的分发和处理与日俱增.图像质量评价课题的研究是其他许多应用的基础，至今图像质量评价课题研究的重点是传统自然图像，因此，针对屏幕图像质量评价的研究就变得非常迫切和必要.客观图像质量评价算法提出的基础建立在标准图像质量评价数据库上.首先构建了一个大规模的屏幕内容图像质量评价数据库（immersive media laboratory screen content image quality database，简称IML-SCIQD）.IML-SCIQD数据库包含参考图像25幅以及经过10种失真处理的1 250幅失真图像.以建立的IML-SCIQD数据库为基础，考虑到屏幕内容图像的图像区域与文本区域的视觉感知差异，在基于自然场景统计的无参考方法的启发下，提出了针对屏幕内容图像的无参考评价算法（natural scene statistics based no reference screen content image quality assessment metric，简称NSNRS）.NSNRS算法首先分别计算图像区域和文本区域的质量分数，再将这两个区域的质量分数结合起来得到整幅失真图像的质量分数.该算法与其他12种经典的客观评价算法，包括全参考算法、部分参考算法与无参考算法，在IML-SCIQD数据库和SIQAD数据库上进行了性能测试和对比，结果表明，所提出的算法优于经典的无参考评价算法；就整个数据库而言，所提出的算法可以达到与全参考方法相当的性能.

    Abstract:

    With the rapid development of multi-device interactive applications, the transmission and processing of screen content image (SCI) is growing every day. Image quality assessment, which is the basis of many other research topics, has mainly focused on traditional natural images so far. Image quality assessment specifically for screen content image is therefore becoming very important and urging. Considering that image quality assessment database is the basis of objective image quality assessment metrics, this paper first constructs a large scale Immersive Media Laboratory screen content image quality database (IML-SCIQD). The IML-SCIQD database contain 25 reference images and 1250 distorted images that are distorted by 10 distortions. Based on the IML-SCIQD database, the visual perception difference of pictorial region and textual region is studied. At the same time, inspired by the idea of natural scene statistics (NSS) based no reference (NR) image quality assessment metrics, a NSS based NR content image quality assessment metric (NSNRS) is proposed. The quality scores of textual region and pictorial region are first computed in the NSNRS metric. Then, the quality scores of these two regions are combined to get the quality score of the whole screen content image. For performance comparison, the proposed metric is compared with 12 state-of-the-art objective image quality assessment metrics, including full reference, reduced reference and no reference algorithms, on the IML-SCIQD database and the SIQAD database. Extensive experiments support that the proposed algorithm outperforms the existing representative no reference techniques, and that the new metric has comparable performance with those full reference metrics for the whole database.

    参考文献
    [1] Lu Y, Li SP, Shen HF. Virtualized screen:A third element for cloud-mobile convergence. IEEE Multimedia, 2011,18(2):4-11.[doi:10.1109/MMUL.2011.33]
    [2] Shen HF, Lu Y, Wu F, Li SP. A high-performance remote computing platform. In:Proc. of the IEEE Int'l Conf. on Pervasive Computing and Communications. 2009. 1-6.[doi:10.1109/PERCOM.2009.4912855]
    [3] Yang H, Fang YM, Yuan Y, Lin WS. Subjective quality evaluation of compressed digital compound images. Journal of Visual Communication and Image Representation, 2015,26:105-114.[doi:10.1016/j.jvcir.2014.11.001]
    [4] Zhu WJ, Ding WP, Xu JZ, Shi YH, Yin BC. Screen content coding based on HEVC framework. IEEE Trans. on Multimedia, 2014,16(5):1316-1326.[doi:10.1109/TMM.2014.2315782]
    [5] Jiang GY, Huang DJ, Wang X, Yu M. Overview on image quality assessment methods. Journal of Electronics & Information Technology, 2010,32(1):219-226(in Chinese with English abstract). http://jeit.ie.ac.cn/CN/abstract/abstract13977.shtml[doi:10. 3724/SP.J.1146.2009.00091]
    [6] Wang ZM. Review of no-reference image quality assessment. Acta Automatica Sinica, 2015,6:1062-1079(in Chinese with English abstract). http://www.aas.net.cn/CN/abstract/abstract18682.shtml[doi:10.16383/j.aas.2015.c140404]
    [7] Vipin K, Bhurchandi KM. No-Reference image quality assessment algorithms:A survey. Optik-Int'l Journal for Light and Electron Optics, 2015,126(11):1090-1097.[doi:10.1016/j.ijleo.2015.02.093]
    [8] Manap RA, Shao L. Non-Distortion-Specific no-reference image quality assessment:A survey. Information Sciences, 2015,301:141-160.[doi:10.1016/j.ins.2014.12.055]
    [9] Chandler DM, Hemami SS. VSNR:A wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. on Image Processing, 2007,16(9):2284-2298.[doi:10.1109/TIP.2007.901820]
    [10] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment:From error visibility to structural similarity. IEEE Trans. on Image Processing, 2004,13(4):600-612.[doi:10.1109/TIP.2003.819861]
    [11] Zhang L, Zhang L, Mou XQ, Zhang D. FSIM:A feature similarity index for image quality assessment. IEEE Trans. on Image Processing, 2011,20(8):2378-2386.[doi:10.1109/TIP.2011.2109730]
    [12] Sheikh HR, Bovik AC. Image information and visual quality. IEEE Trans. on Image Processing, 2006,15(2):430-444.[doi:10. 1109/TIP.2005.859378]
    [13] Moorthy AK, Bovik AC. Statistics of natural image distortions. In:Proc. of the IEEE Int'l Conf. on Acoustics, Speech and Signal Processing (ICASSP). 2010. 962-965.[doi:10.1109/ICASSP.2010.5495298]
    [14] Xu L, Lin WS, Jay Kuo C-C. Visual Quality Assessment by Machine Learning. Singapore:Springer-Verlag, 2015. 1-142.[doi:10. 1007/978-981-287-468-9]
    [15] Moorthy AK, Bovik AC. Blind image quality assessment:From natural scene statistics to perceptual quality. IEEE Trans. on Image Processing, 2011,20(12):3350-3364.[doi:10.1109/TIP.2011.2147325]
    [16] Liu LX, Liu B, Huang H, Bovik AC. No-Reference image quality assessment based on spatial and spectral entropies. Signal Processing:Image Communication, 2014,29(8):856-863.[doi:10.1016/j.image.2014.06.006]
    [17] Li CF, Bovik AC, Wu XJ. Blind image quality assessment using a general regression neural network. IEEE Trans. on Neural Networks, 2011,22(5):793-799.[doi:10.1109/TNN.2011.2120620]
    [18] Ye P, Doermann D. No-Reference image quality assessment using visual codebooks. IEEE Trans. on Image Processing, 2012,21(7):3129-3138.[doi:10.1109/TIP.2012.2190086]
    [19] Xue WF, Zhang L, Mou XQ. Learning without human scores for blind image quality assessment. In:Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2013. 995-1002.[doi:10.1109/CVPR.2013.133]
    [20] Yang H, Fang YM, Lin WS. Perceptual quality assessment of screen content images. IEEE Trans. on Image Processing, 2015, 21(11):4408-4421.[doi:10.1109/TIP.2015.2465145]
    [21] Ni ZK, Ma L, Zeng HQ, Cai CH, Ma K-K. Gradient direction for screen content image quality assessment. IEEE Signal Processing Letters, 2016,23(10):1394-1398.[doi:10.1109/LSP.2016.2599294]
    [22] Gu K, Wang SQ, Yang H, Lin WS, Zhai GT, Yang XK, Zhang WJ. Saliency-Guided quality assessment of screen content images. IEEE Trans. on Multimedia, 2016,18(6):1098-1110.[doi:10.1109/TMM.2016.2547343]
    [23] Wang SQ, Gu K, Zhang XF, Lin WS, Ma SW, Gao W. Reduced-Reference quality assessment of screen content images. IEEE Trans. on Circuits and Systems for Video Technology, 2016,PP(99):1-1.[doi:10.1109/TCSVT.2016.2602764]
    [24] Sheikh HR, Wang Z, Cormack L, Bovik AC. LIVE image quality assessment database release 2. 2005. http://live.ece.utexas.edu/research/quality
    [25] Larson EC, Chandler DM. Categorical image quality (CSIQ) database. 2009. http://vision.okstate.edu/csiq
    [26] Ponomarenko N, Egiazarian K. Tampere image database 2008 TID2008. 2008. http://www.ponomarenko.info/tid2008.html
    [27] Ponomarenko N, Ieremeiev O, Tampere V. Image database (TID2013). 2013. http://www.ponomarenko.info/tid2013.html
    [28] ITU, Iternational Telecommunication Union. BT.500:Methodology for the subjective assessment of the quality of television pictures. 2002. https://www.itu.int/rec/R-REC-BT.500
    [29] NTIA/ITS. Web-Enabled subjective test (WEST) software. 2014. http://www.its.bldrdoc.gov/resources/video-quality-research/web-enabled-subjective-test-west.aspx
    [30] Seshadrinathan K, Soundararajan R, Bovik AC, Cormack LK. Study of subjective and objective quality assessment of video. IEEE Trans. on Image Processing, 2010,19(6):1427-1441.[doi:10.1109/TIP.2010.2042111]
    [31] Yang H, Lin WS, Deng CW. Image activity measure (IAM) for screen image segmentation. In:Proc. of the IEEE Int'l Conf. on Image Processing. 2012. 1569-1572. https://sites.google.com/site/yanghuan689/
    [32] Moorthy AC, Bovik AC. Blind image quality assessment:From scene statistics to perceptual quality. IEEE Trans. on Image Processing, 2011,20(12):3350-3364.[doi:10.1109/TIP.2011.2147325]
    [33] Moorthy AK, Bovik AC. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 2010,17(5):513-516.[doi:10.1109/LSP.2010.2043888]
    [34] Mittal A, Moorthy AK, Bovik AC. No-Reference image quality assessment in the spatial domain. IEEE Trans. on Image Processing, 2012,21(12):4695-4708.[doi:10.1109/TIP.2012.2214050]
    [35] Chang CC, Lin CJ. LIBSVM:A library for support vector machines. 2001. http://www.csie.ntu.edu.tw/~cjlin/index.html
    附中文参考文献:
    [5] 蒋刚毅,黄大江,王旭,郁梅.图像质量评价方法研究进展.电子与信息学报,2010,32(1):219-226. http://jeit.ie.ac.cn/CN/abstract/abstract13977.shtml[doi:10.3724/SP.J.1146.2009.00091]
    [6] 王志明.无参考图像质量评价综述.自动化学报,2015,6:1062-1079. http://www.aas.net.cn/CN/abstract/abstract18682.shtml[doi:10.16383/j.aas.2015.c140404]
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

朱映映,曹磊,王旭.无参考屏幕内容图像质量评价.软件学报,2018,29(4):973-986

复制
分享
文章指标
  • 点击次数:4254
  • 下载次数: 8422
  • HTML阅读次数: 3471
  • 引用次数: 0
历史
  • 收稿日期:2017-04-30
  • 最后修改日期:2017-06-26
  • 在线发布日期: 2017-11-29
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19892507位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号