scholarly journals Multi-View 3D CG Image Quality Assessment for Contrast Enhancement Based on S-CIELAB Color Space

2017 ◽  
Vol E100.D (7) ◽  
pp. 1448-1462 ◽  
Author(s):  
Norifumi KAWABATA ◽  
Masaru MIYAO
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Image Quality Assessment ◽  
Color Space ◽  
Cielab Color Space ◽  
Cielab Color

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 Methods to Increase the Contrast of the Image with Preserving the Visual Quality

2021 ◽  
Vol 6 (2) ◽  
pp. 140-145
Author(s):  
Mykola Maksymiv ◽  
◽  
Taras Rak
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Image Quality Assessment ◽  
Visual Quality ◽  
Image Contrast ◽  
Weighted Sum ◽  
Enhancement Method ◽  
Optimal Target ◽  
Statistical Metrics

Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.

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.

Image quality assessment for contrast enhancement evaluation

2017 ◽  
Vol 77 ◽  
pp. 61-66 ◽  
Author(s):  
Ayub Shokrollahi ◽  
Ahmad Mahmoudi-Aznaveh ◽  
Babak Mazloom-Nezhad Maybodi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Image Quality Assessment

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 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.

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