No Reference Screen Content Image Quality Assessment
Author:
Affiliation:

Clc Number:

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)

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    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.

    Reference
    Related
    Cited by
Get Citation

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

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:April 30,2017
  • Revised:June 26,2017
  • Adopted:
  • Online: November 29,2017
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063