Open AccessRegularity of spectral residual for reduced reference image quality assessment
Author(s) -
Liu Delei,
Li Fuzhong,
Song Houbing
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0593
Subject(s) - metric (unit) , residual , artificial intelligence , pattern recognition (psychology) , image quality , image (mathematics) , feature (linguistics) , computer science , wavelet , computer vision , dimension (graph theory) , mathematics , algorithm , linguistics , operations management , philosophy , pure mathematics , economics
Inspired by the facts that visual saliency captures more attention and spectral residual (SR) can indicate the saliency of the image, a novel reduced‐reference image quality assessment metric is proposed based on the regularity of the SR. The orientation and frequency components of an image are first extracted in wavelet domain. Then SR is obtained to represent the saliency of the component. Next fractal dimension is adopted to encode SR and concatenated as the image features. Finally, the feature differences between reference image and distorted one are pooled as the quality score. The proposed metric is evaluated on four largest image databases (TID2013, TID2008, CSIQ, and LIVE databases), and experimental results confirm that the proposed metric has a good performance.