Printed line perceptual quality assessment based on fussy neural network

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument

Volumn 34, Issue 12, 2013, Pages 2675-2683

  Kong, Lingjun ^a,b   Liu, Zhen ^b   Sun, Xiaopeng ^b   Jiang, Zhongmin ^b  

^a Shanghai Publishing and Printing College   (China)

^b UNIVERSITY OF SHANGHAI FOR SCIENCE AND TECHNOLOGY   (China)

Author keywords

Comprehensive assessment; Fussy neural network; Machine vision; Perceptual quality; Printed line

Indexed keywords

COMPREHENSIVE ASSESSMENT; OBJECTIVE ASSESSMENT; OBJECTIVE MEASUREMENT; PERCEPTUAL ASSESSMENT; PERCEPTUAL QUALITY; PRINTED LINE; SUBJECTIVE ASSESSMENTS; SUBJECTIVE PERCEPTUAL QUALITY;

IMAGE QUALITY; NEURAL NETWORKS; QUALITY MANAGEMENT;

COMPUTER VISION;

EID: 84891940217     PISSN: 02543087     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. ZHANG H, WANG Y N, ZHOU B W. Research on foreign substance detection system for medicinal solution based on machine vision [J]. Chinese Journal of Scientific Instrument, 2009, 30 (3): 548-553.
2. XIANG SH B, SU G D, CHEN J SH, et al. Brick stack anomaly detection and recognition based on machine vision [J]. Acta Optica Sinica, 2011, 31 (7): 184-190.
3. DU CH J, SUN D W. Learning techniques used in computer vision for food quality evaluation: A review [J]. Journal of Food Engineering, 2006, 72 (1): 39-55.
4. ISO/IEC 13660. Information technology office equipment measurement of image quaiity attributes for hardcopy output Binary monochrome text and graphic images [R]. 1st ed. 2001, 9: 1
5. WU W CH, DALAL E N. Perception-based line quality measurement [C]. Electronic Imaging Symposium, San Jose, CA, 2005: 111-122.
6. TSE M K. A predictive model for text quality analysis: Case study [C]. NIP23, Alaska, 2007: 419-423.
7. JIANG G P, XU Y F, GUO G, et al. Research on the line sharpness assessment of digital prints [J]. Chinese Printing and Packaging Research, 2011, 2 (Suppl. 1): 221-225.
8. XU Y F, LIU H X, HUANG M, et al. Study on a correlation model for text perceptual distinctness [J]. Acta Optica Sinica, 2012, 32 (12): 292-298.
9. YAO H G, KONG L J, ZHOU Y M, et al. Digital printing quality measurement and assessment [M]. Beijing: Printing Industry Press, 2012: 141-162.
10. LIU H. Judging boiler combustion stability based on flame image and fuzzy neural network [J]. Chinese Journal of Scientific Instrument, 2008, 29 (6): 1280-1284.
11. GAO SH ZH, GAO X W, WANG J SH. Soft-sensor modeling of polymerizing process based on multi T-S fuzzy neural network optimized with harmony search algorithm [J]. Chinese Journal of Scientific Instrument, 2011, 32 (3): 495-500.
12. CHANG P C, LIU CH H, FAN CH Y. Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry [J]. Knowledge-Based Systems, 2009, 22 (5): 344-355.
13. LEISTI T, RADUN J, VIRTANEN T, et al. Subjective experience of Image quality: Attributes, definitions and decision making of subjective image quality [C]. Proc. SPIE 7242, Image Quality and System Performance VI, San Jose, CA, 2009.
14. ENGELDREM P G. Psychometric scaling: A Toolkit for Imaging Systems Development [M]. Winchester: IMcotek Press, 2000: 79-145.
15. GY/T 134-1998. The method for Subjective assessment of the quality of digital television picture [R]. 1998.
16. GAO X B, LU W. Vision information quality assessment method [M]. Xi'an: Xidian University Press, 2010: 133-141.