Image quality assessment algorithm based on visual saliency model

CHEN Hong-da; DING Yong

doi:10.13873/j.1000-9787(2018)10-0123-04

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Image quality assessment algorithm based on visual saliency model

更新时间：2025-09-11

- Image quality assessment algorithm based on visual saliency model
- Issue 10, Pages: 123-126(2018)
- 作者机构：
  
  浙江大学超大规模集成电路设计研究所
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2018)10-0123-04
  CLC： TP391.41
- Published：2018
- 稿件说明：
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CHEN Hong-da, DING Yong. Image quality assessment algorithm based on visual saliency model[J]. 2018, (10): 123-126.
DOI：

CHEN Hong-da, DING Yong. Image quality assessment algorithm based on visual saliency model[J]. 2018, (10): 123-126. DOI： 10.13873/j.1000-9787(2018)10-0123-04.

摘要

为了获得符合人眼感知的图像质量

提出一种基于视觉显著模型的全参考图像质量评价方法。视觉显著模型分为2层:最小可觉差(JND)模型提取的梯度感知图和超像素分割算法提取的权重感知图。在2层感知图融合的基础上

度量参考图像和失真图像在感知图上的特征差异。利用支持向量回归(SVR)的方法

获得客观质量分数。实验结果表明:提出的方法在图像质量上的客观评价表现优于当前的主流算法

对不同失真类型和不同数据库均与人眼主观评价相一致。

Abstract

In order to achieve image quality which is consistent with human eyes perception

a full reference image quality evaluation method based on visual saliency model is proposed. The proposed saliency model is divided into two layers: the first layer is that the gradient perception map extracted by just noticeable difference（ JND） model

the second layer is that the weight perception map extracted by superpixel segmentation algorithm.On the basis of fusion of two layers perception maps

the difference of feature is measured between perceptual maps of reference image and distorted image. Support vector regression（ SVR） is used to obtain objective quality fraction. The experimental results indicate that objective assessment of the proposed method is better than mainstream algorithm in image quality

and it is consistent with human eyes subjective assessment for different types of distortion and different databases.

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