Morphological image segmentation applied to video quality assessment

被引:4
|
作者
Lotufo, RD [1 ]
da Silva, WDF [1 ]
Falcao, AX [1 ]
Pessoa, ACF [1 ]
机构
[1] Univ Estadual Campinas, FEEC, UNICAMP, BR-13081970 Campinas, SP, Brazil
关键词
mathematical morphology; segmentation; video quality assessment; connected filters;
D O I
10.1109/SIBGRA.1998.722793
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This paper presents a methodology to morphological video segmentation used as part of a method to estimate the subjective video quality assessment. The segmentation goal is to partition the sequence frames in three different regions: homogeneous, border and texture. Previous work [4] has shown the importance of computing video imparment measurements to each of these regions separately, in contrast to use global measurements. The segmentation approach presented in this work uses a collection of morphological tools such as connected smoothing filters, morphological gradients, and watershed. Special attention is devoted to describe the recent concept of connected filters, used as the kernel of the morphological segmentation algorithms. The performance of two morphological segmentation paradigms, one based on the flat zone, and the other based on the watershed-plus-markers approach are evaluated and compared to other segmentation methodology used in video quality assessment.
引用
收藏
页码:468 / 475
页数:8
相关论文
共 50 条
  • [21] A pansharpened image quality assessment using segmentation procedure
    Aghapour Maleki, Shiva
    Ghassemian, Hassan
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (11) : 4157 - 4176
  • [22] Design considerations for image segmentation quality assessment measures
    Paglieroni, DW
    [J]. PATTERN RECOGNITION, 2004, 37 (08) : 1607 - 1617
  • [23] Cardiac MR image segmentation: Quality assessment of STACS
    Pluempitiwiriyawej, C
    Moura, JMF
    Wu, YJL
    Ho, C
    [J]. 2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 AND 2, 2004, : 828 - 831
  • [24] Image quality assessment for video stream recognition systems
    Chernov, Timofey S.
    Razumnuy, Nikita P.
    Kozharinov, Alexander S.
    Nikolaev, Dmitry P.
    Arlazarov, Vladimir V.
    [J]. TENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2017), 2018, 10696
  • [25] Scope of validity of PSNR in image/video quality assessment
    Huynh-Thu, Q.
    Ghanbari, M.
    [J]. ELECTRONICS LETTERS, 2008, 44 (13) : 800 - U35
  • [26] Analysis of Public Image and Video Databases for Quality Assessment
    Winkler, Stefan
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2012, 6 (06) : 616 - 625
  • [27] Morphological refinement of an image segmentation
    Iwanowski, M
    Soille, P
    [J]. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS, 2005, 3691 : 538 - 545
  • [28] Morphological image enhancement and segmentation
    Terol-Villalobos, IR
    [J]. ADVANCES IN IMAGING AND ELECTRON PHYSICS, VOL 118, 2001, 118 : 207 - 273
  • [29] FUSION OF IMPRECISE DATA APPLIED TO IMAGE QUALITY ASSESSMENT
    Guettari, Nadjib
    Capelle-Laize, Anne Sophie
    Carre, Philippe
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 521 - 525
  • [30] IMPORT OF DISTORTION ON SALIENCY APPLIED TO IMAGE QUALITY ASSESSMENT
    Wang, Qing
    Xu, Lin
    Chen, Qiang
    Sun, Quansen
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 1165 - 1169