A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information

Abstract A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.

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A New Image Quality Assessment Method Based on SSIM and TV Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.65.542 ◽

2011 ◽

Vol 65 ◽

pp. 542-550

Author(s):

Lu Lu Pang ◽

Cong Li Li ◽

De Ning Qi ◽

Tao Zou

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Explicit Knowledge ◽

Image Quality Assessment ◽

Structural Similarity ◽

Assessment Method ◽

Reference Image ◽

Distorted Image ◽

Quality Assessment Method ◽

Reference Images

In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.

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A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms

Multimedia Tools and Applications ◽

10.1007/s11042-021-10573-0 ◽

2021 ◽

Author(s):

Baisakhi Sur Phadikar ◽

Amit Phadikar ◽

Subro S. Thakur

Keyword(s):

Image Quality ◽

Image Retrieval ◽

Quality Assessment ◽

Image Quality Assessment ◽

Comprehensive Assessment ◽

Content Based Image Retrieval ◽

Reference Image ◽

Full Reference

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No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Journal of Imaging ◽

10.3390/jimaging6080075 ◽

2020 ◽

Vol 6 (8) ◽

pp. 75

Author(s):

Domonkos Varga

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Reference Image ◽

Phase Congruency ◽

Perceptual Features ◽

Proposed Model ◽

Dark Channel ◽

Reference Images ◽

Spatial Domains

The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

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