A no-reference quality assessment for contrast-distorted image based on improved learning method

Wu, Yan; Yonghe, Zhu; Yang, Yang; Zhang, Weimin; Yu, Nenghai

doi:10.1007/s11042-018-6524-1

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…1, there are 106859 bits, 131466 bits, 309087 bits in the lesion area of three medical images respectively, the corresponding value of no-reference contrast distortion image quality assessment (NR-CDIQA) is 2.6808, 2.5863, and 2.8334 respectively, and the corresponding value of no-reference improved contrast distortion image quality assessment (NR-ICDIQA) is 2.8265, 2.9753, and 2.8602 respectively. Here, NR-CDIQA [Fang, Ma, Wang et al (2014)] and NR-ICDIQA [Wu, Zhu, Yang et al (2018)] are noreference image quality assessment (IQA) method based on the principle of natural scene statistics (NSS) only for contrast enhancement. NR-CDIQA and NR-ICDIQA methods can effectively assess the quality of contrast-enhancement images.…”

Section: Experiments Results In the Lesion Areamentioning

confidence: 99%

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

Xiao¹,

Yang²,

Li³

et al. 2019

Computers, Materials &Amp; Continua

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The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients' privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for medical images. Secondly, the data is embedded into the lesion area by improved histogram shifting method to enhance the contrast of medial image's lesion area. Lastly, the rest of data is embedded into the non-lesion area by the high-capacity embedding method to achieve the higher payload. At the receiving end, data can be extracted completely and images can be recovered losslessly by the third party with right. Experimental results have shown that the method of lesion extraction has an advantage over the existing segmentation methods in medical images. The image quality is improved well and the performance of contrast enhancement in the lesion area is better than other RDH methods with contrast enhancement.

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Section: Experiments Results In the Lesion Areamentioning

confidence: 99%

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

Xiao¹,

Yang²,

Li³

et al. 2019

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because data is embedded into empty bins in the lesion area, there is the maximum embedding bits. As shown in Table 1, there are 99488 bits, 132437 bits and 52438 bits in the lesion area of three medical images respectively, the corresponding value of no-reference contrast distortion image quality assessment (NR-CDIQA) [31] is 2.9039, 2.5891 and 2.8980 respectively, and the corresponding value of no-reference improved contrast distortion image quality assessment (NR-ICDIQA) [32] is 2.9894, 2.9784 and 2.9806 respectively. Here, NR-CDIQA and NR-ICDIQA are no-reference image quality assessment (IQA) methods based on the principle of natural scene statistics (NSS) only for contrast enhancement.…”

Section: A Experiments Of Rdhmentioning

confidence: 99%

A Secure and High Visual-Quality Framework for Medical Images by Contrast-Enhancement Reversible Data Hiding and Homomorphic Encryption

et al. 2019

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Medical data security is facing great challenges in medical applications due to the open internet and the semi-trusted cloud. For the sake of privacy protection and the security of medical images, this paper proposes a secure and high visual-quality framework for medical images. In this framework, a novel reversible data hiding (RDH) based on lesion extraction embeds privacy data into medical images for privacy protection and image quality improvement, homomorphic encryption based on chaotic map encrypts images for medical image security. The experiments have shown that the proposed framework increases the security of medical data and improves the visual quality of medical images significantly. The proposed RDH in this framework outperforms the other RDH methods with contrast enhancement and the proposed encryption scheme increases image security well. INDEX TERMSReversible data hiding, homomorphic encryption, contrast enhancement, security, medical images.

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No-reference Quality Assessment for Contrast-distorted Images Based on Gray and Color-gray-difference Space

Yang

Ding

Cheng

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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No-reference image quality assessment is a basic and challenging problem in the field of image processing. Among them, contrast distortion has a great impact on the perception of image quality. However, there are relatively few studies on no-reference quality assessment of contrast-distorted images. This paper proposes a no-reference quality assessment algorithm for contrast-distorted images based on gray and color-gray-difference(CGD) space. In terms of gray space, we consider the local and global aspects, and use the distribution characteristics of the grayscale histogram to represent global features, while local feature is described by the fusion of Local Binary Patterns (LBP) operator and gradient; In terms of CGD space, we first randomly extract patches from the entire image, and then extract appropriate quality perception features in the patch’s CGD histogram. Finally, AdaBoosting back propagation (BP) neural network is used to train the prediction model to predict the quality of the contrast-distorted image. Extensive analysis and cross-validation are carried out on five contrast-related image databases, and the experimental results have proved the superiority of this method compared with recent related algorithms.

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A no-reference quality assessment for contrast-distorted image based on improved learning method

Cited by 3 publications

References 26 publications

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

A Secure and High Visual-Quality Framework for Medical Images by Contrast-Enhancement Reversible Data Hiding and Homomorphic Encryption

No-reference Quality Assessment for Contrast-distorted Images Based on Gray and Color-gray-difference Space

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