An Image Quality Assessment Method based on Sparse Neighbor Significance

Selcuk Aydi

2022

Abstract

In this paper, the image quality assessment problem is tackled from a sparse coding perspective, and a new automated image quality assessment algorithm is presented. Specifically, the input image is first divided into non-overlapping blocks and sparse coding is used to reconstruct a central sub-block using the neighboring sub-blocks as dictionaries. The resulting 2D sparse vectors from each neighboring sub-block, are devised as significance maps that are then used in similarity measures between the reference and distorted images. The proposed method is compared against various recently introduced shallow and deep methods across four datasets and multiple distortion types. The experimental results that have been obtained show that it possesses a strong correlation with the Human Visual System and outperforms its counterparts.

Paper Citation

in Harvard Style

Aydi S. (2022). An Image Quality Assessment Method based on Sparse Neighbor Significance. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 34-44. DOI: 10.5220/0011058700003209

in Bibtex Style

@conference{improve22,
author={Selcuk Aydi},
title={An Image Quality Assessment Method based on Sparse Neighbor Significance},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2022},
pages={34-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011058700003209},
isbn={978-989-758-563-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - An Image Quality Assessment Method based on Sparse Neighbor Significance
SN - 978-989-758-563-0
AU - Aydi S.
PY - 2022
SP - 34
EP - 44
DO - 10.5220/0011058700003209