scholarly journals An Improved SPSIM Index for Image Quality Assessment

2021 ◽  
Author(s):  
Mariusz Frackiewicz ◽  
Grzegorz Szolc ◽  
Henryk Palus
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Space ◽  
Image Databases ◽  
Assessment Process ◽  
Strongly Correlated ◽  
Quality Indices ◽  
The Third ◽  
Similarity Maps

Objective Image Quality Assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by MOS/DMOS scores. One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of the rectangular pixel grid.The authors in this paper have been proposed three modifications of SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from the algorithm for computing the MDSI index. The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained on many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

scholarly journals An Improved SPSIM Index for Image Quality Assessment

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 518
Author(s):  
Mariusz Frackiewicz ◽  
Grzegorz Szolc ◽  
Henryk Palus
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Space ◽  
Similarity Index ◽  
Mean Opinion Score ◽  
Assessment Process ◽  
Mean Deviation ◽  
Strongly Correlated ◽  
Opinion Score

Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

scholarly journals Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space

2019 ◽  
Vol 9 (12) ◽  
pp. 2499
Author(s):  
Yiling Tang ◽  
Shunliang Jiang ◽  
Shaoping Xu ◽  
Tingyun Liu ◽  
Chongxi Li
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Space ◽  
Support Vector ◽  
Feature Maps ◽  
Hsv Color Space ◽  
Window Method ◽  
Blind Image Quality Assessment ◽  
Blind Image

To improve the evaluation accuracy of the distorted images with various distortion types, an effective blind image quality assessment (BIQA) algorithm based on the multi-window method and the HSV color space is proposed in this paper. We generate multiple normalized feature maps (NFMs) by using the multi-window method to better characterize image degradation from the receptive fields of different sizes. Specifically, the distribution statistics are first extracted from the multiple NFMs. Then, Pearson linear correlation coefficients between spatially adjacent pixels in the NFMs are utilized to quantify the structural changes of the distorted images. Weibull model is utilized to capture distribution statistics of the differential feature maps between the NFMs to more precisely describe the presence of the distortions. Moreover, the entropy and gradient statistics extracted from the HSV color space are employed as a complement to the gray-scale features. Finally, a support vector regressor is adopted to map the perceptual feature vector to image quality score. Experimental results on five benchmark databases demonstrate that the proposed algorithm achieves higher prediction accuracy and robustness against diverse synthetically and authentically distorted images than the state-of-the-art algorithms while maintaining low computational cost.

scholarly journals Center-Emphasized Visual Saliency and a Contrast-Based Full Reference Image Quality Index

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 296 ◽  
Author(s):  
Md. Layek ◽  
A. Uddin ◽  
Tuyen Le ◽  
TaeChoong Chung ◽  
Eui-Nam Huh
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Image Quality Assessment ◽  
Visual Saliency ◽  
Human Vision ◽  
Reference Image ◽  
Full Reference ◽  
Human Attention ◽  
Similarity Maps

Objective image quality assessment (IQA) is imperative in the current multimedia-intensive world, in order to assess the visual quality of an image at close to a human level of ability. Many parameters such as color intensity, structure, sharpness, contrast, presence of an object, etc., draw human attention to an image. Psychological vision research suggests that human vision is biased to the center area of an image and display screen. As a result, if the center part contains any visually salient information, it draws human attention even more and any distortion in that part will be better perceived than other parts. To the best of our knowledge, previous IQA methods have not considered this fact. In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast; however, we give extra attention to the center by increasing the sensitivity of the similarity maps in that region. We evaluated our method on three large-scale popular benchmark databases used by most of the current IQA researchers (TID2008, CSIQ and LIVE), having a total of 3345 distorted images with 28 different kinds of distortions. Our method is compared with 13 state-of-the-art approaches. This comparison reveals the stronger correlation of our method with human-evaluated values. The prediction-of-quality score is consistent for distortion specific as well as distortion independent cases. Moreover, faster processing makes it applicable to any real-time application. The MATLAB code is publicly available to test the algorithm and can be found online at.

No-reference image quality assessment using joint color space statistical and texture feature

2018 ◽  
Vol 26 (4) ◽  
pp. 916-926 ◽  
Author(s):  
范赐恩 FAN Ci-en ◽  
冉杰文 RAN Jie-wen ◽  
颜佳 YAN Jia ◽  
邹炼 ZOU Lian ◽  
石文轩 SHI Wen-xuan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Color Space ◽  
Texture Feature ◽  
Reference Image

scholarly journals Deep Superpixel-Based Network For Blind Image Quality Assessment

2021 ◽  
Author(s):  
Guangyi Yang ◽  
Yang Zhan ◽  
Yuxuan Wang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Feature Fusion ◽  
Assessment Process ◽  
Semantic Features ◽  
Multi Scale ◽  
Blind Image Quality Assessment ◽  
Blind Image

Abstract The goal in a blind image quality assessment (BIQA) method is to simulate the process of evaluating images by human eyes and accurately assess the quality of the image. Although many methods effectively identify degradation, they do not fully consider the semantic content in images resulting in distortion. In order to fill this gap, we propose a deep adaptive superpixel-based network, namely DSN-IQA, to assess the quality of image based on multi-scale and superpixel segmentation. The DSN-IQA can adaptively accept arbitrary scale images as input images, making the assessment process similar to human perception. The network uses two models to extract multi-scale semantic features and generate a superpixel adjacency map. These two elements are united together via feature fusion to accurately predict image quality. Experimental results on different benchmark databases demonstrate that our method is highly competitive with other methods when assessing challenging authentic image databases. Also, due to adaptive deep superpixel-based network, our method accurately assesses images with complicated distortion, much like the human eye.

scholarly journals Image quality assessment

2022 ◽  
pp. 4-15
Author(s):  
Y. I. Golub
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Digital Images ◽  
Image Databases ◽  
Automated Systems ◽  
Digital Formats ◽  
Proximity Measures ◽  
Visual Assessments

Quality assessment is an integral stage in the processing and analysis of digital images in various automated systems. With the increase in the number and variety of devices that allow receiving data in various digital formats, as well as the expansion of human activities in which information technology (IT) is used, the need to assess the quality of the data obtained is growing. As well as the bar grows for the requirements for their quality.The article describes the factors that deteriorate the quality of digital images, areas of application of image quality assessment functions, a method for normalizing proximity measures, classes of digital images and their possible distortions, image databases available on the Internet for conducting experiments on assessing image quality with visual assessments of experts.

A Novel Hybrid Image Quality Assessment Algorithm

2011 ◽  
Vol 4 (4) ◽  
pp. 107-108
Author(s):  
Deepa Maria Thomas ◽  
◽  
S. John Livingston
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment
Sign in / Sign up

Export Citation Format

Share Document