id: 05972689
dt: a
an: 05972689
au: Ponomarenko, Nikolay; Jin, Lina; Lukin, Vladimir; Egiazarian, Karen
ti: Self-similarity measure for assessment of image visual quality.
so: Blanc-Talon, Jacques (ed.) et al., Advances concepts for intelligent vision
    systems. 13th international conference, ACIVS 2011, Ghent, Belgium,
    August 22‒25, 2011. Proceedings. Berlin: Springer (ISBN
    978-3-642-23686-0/pbk). Lecture Notes in Computer Science 6915, 459-470
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: full reference image visual quality; human visual system; human perception;
    discrete cosine transform; image self-similarity
ci: 
li: doi:10.1007/978-3-642-23687-7_42
ab: Summary: An opportunity of using self-similarity in evaluation of image
    visual quality is considered. A method for estimating self-similarity
    for a given image fragment that takes into account contrast sensitivity
    function is proposed. Analytical expressions for describing the
    proposed parameter distribution are derived, and their importance to
    human vision system based image visual quality full-reference
    evaluation is proven. A corresponding metric is calculated and a mean
    squared difference for the considered parameter maps in distorted and
    reference images is considered. Correlation between this metric and
    mean opinion score (MOS) for five largest openly available specialized
    image databases is calculated. It is demonstrated that the proposed
    metric provides a correlation at the level of the best known metrics of
    visual quality. This, in turn, shows an importance of fragment
    self-similarity in image perception.
rv: