Image Quality Assessment in Reversible Data Hiding with Contrast Enhancement

2017 ◽  
pp. 290-302 ◽  
Author(s):  
Hao-Tian Wu ◽  
Shaohua Tang ◽  
Yun-Qing Shi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Image Quality Assessment

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.

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

scholarly journals Lossless Contrast Enhancement of Color Images with Reversible Data Hiding

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 910 ◽  
Author(s):  
Hao-Tian Wu ◽  
Yue Wu ◽  
Zhihao Guan ◽  
Yiu-ming Cheung
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Data Hiding ◽  
Information Entropy ◽  
Reversible Data Hiding ◽  
Color Images ◽  
Experimental Results ◽  
Original Version ◽  
Color Model ◽  
Made In

Recently, lossless contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version by maintaining information entropy in it. As most of the lossless CE methods are proposed for grayscale images, artifacts are probably introduced after directly applying them to color images. For instance, color distortions may be caused after CE is separately conducted in each channel of the RGB (red, green, and blue) model. To cope with this issue, a new scheme is proposed based on the HSV (hue, saturation, and value) color model. Specifically, both hue and saturation components are kept unchanged while only the value component is modified. More precisely, the ratios between the RGB components are maintained while a reversible data hiding method is applied to the value component to achieve CE effects. The experimental results clearly show CE effects obtained with the proposed scheme, while the original color images can be perfectly recovered. Several metrics including image entropy were adopted to measure the changes made in CE procedure, while the performances were compared with those of one existing scheme. The evaluation results demonstrate that better image quality and increased information entropy can be simultaneously achieved with our proposed scheme.

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

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Image quality assessment based on structural orientation information

2010 ◽  
Vol 30 (6) ◽  
pp. 1622-1625
Author(s):  
Qiang WANG ◽  
De-qun LIANG ◽  
Sheng BI ◽  
Yu BO
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Structural Orientation ◽  
Orientation Information

Image quality assessment based on local invariant features

2013 ◽  
Vol 32 (12) ◽  
pp. 3369-3372 ◽  
Author(s):  
Ya-zhou YANG ◽  
Xiao-qing YING ◽  
Guang-quan CHENG ◽  
Dan TU
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Invariant ◽  
Invariant Features ◽  
Local Invariant Features
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