Image quality assessment using edge based features

被引:20
|
作者
Attar, Abdolrahman [1 ]
Shahbahrami, Asadollah [2 ]
Rad, Reza Moradi [3 ]
机构
[1] Lincoln Univ, Sch Comp Sci, Computat Intelligence Lab, Lincoln LN6 7TS, England
[2] Univ Guilan, Dept Comp Engn, Fac Engn, POB 3756-41635, Rasht, Iran
[3] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia
关键词
Image quality assessment; Edge based features; Full reference; STRUCTURAL SIMILARITY; STATISTICS;
D O I
10.1007/s11042-015-2663-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There are many applications for Image Quality Assessment (IQA) in digital image processing. Many techniques have been proposed to measure the quality of an image such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Structural Similarity Index Measurement (MSSIM). In this paper, a new technique, namely, Edge Based Image Quality Assessments (EBIQA) is proposed. The proposed technique is based on different edge features which are extracted from original (distortion free) and distorted images. The new approach was implemented and tested using different images which have been taken from A57 and WIQ image databases. The experimental results show that the functionality of the EBIQA technique is better than the state of art IQA techniques. The proposed technique is consistent with the mean opinion score which makes it suitable for automatic image quality assessment.
引用
收藏
页码:7407 / 7422
页数:16
相关论文
共 50 条
  • [21] Full-reference image quality assessment based on image segmentation with edge feature
    Shi, Zaifeng
    Zhang, Jiaping
    Cao, Qingjie
    Pang, Ke
    Luo, Tao
    SIGNAL PROCESSING, 2018, 145 : 99 - 105
  • [22] An Image Quality Assessment Metric Based On Non-Shift Edge
    Xue, Wufeng
    Mou, Xuanqin
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011,
  • [23] No-Reference Color Image Quality Assessment Using HOSVD Based Features and Neural Networks
    Suliman, Wael
    Deriche, Mohamed
    Mohandes, Mohamed
    PROCEEDINGS OF THE 2020 17TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD 2020), 2020, : 474 - 478
  • [24] An edge-based color image retrieval by using multiple features
    Wang, Xiang-Yang
    Chen, Jing-Wei
    Yu, Yong-Jian
    Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2010, 23 (02): : 216 - 221
  • [25] Image Reconstruction for Quality Assessment of Edge Detectors
    Govindarajan, Barghavi
    Panetta, Karen A.
    Agaian, Sos
    2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6, 2008, : 691 - +
  • [26] Edge Strength Similarity for Image Quality Assessment
    Zhang, Xuande
    Feng, Xiangchu
    Wang, Weiwei
    Xue, Wufeng
    IEEE SIGNAL PROCESSING LETTERS, 2013, 20 (04) : 319 - 322
  • [27] Actived Edge Strength for Image Quality Assessment
    Gao, Minjuan
    Zhang, Xuande
    2019 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS), 2019,
  • [28] Recent advances in image processing using image texture features for food quality assessment
    Jackman, Patrick
    Sun, Da-Wen
    TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2013, 29 (01) : 35 - 43
  • [29] No-Reference Image Quality Assessment Using the Statistics of Global and Local Image Features
    Varga, Domonkos
    ELECTRONICS, 2023, 12 (07)
  • [30] REFERENCELESS QUALITY ASSESSMENT FOR CONTRAST DISTORTED IMAGE USING HYBRID FEATURES
    Deng, Bin
    Zhang, Xinfeng
    Wang, Shanshe
    Pan, Xiaofei
    Ma, Siwei
    Xiong, Ruiqin
    2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2019, : 2354 - 2358