Image Compression Based on Deep Learning: A Review

Yasin, Hajar Maseeh; Abdulazeez, Adnan Mohsin

doi:10.9734/ajrcos/2021/v8i130193

Cited by 17 publications

(7 citation statements)

References 62 publications

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“…AVM (x,y) = θ1Ab(x,y) + θ2As(x,y) + θ3Ae(x,y) (6) Yes Yes Yes [14] Yes Yes Yes [15] Yes [16] Yes Yes [17] Yes [18] Yes Yes [19] Yes Yes [20] Yes Yes Yes Yes Yes [21] Yes Yes [22] Yes [23] Yes Yes…”

Section: Multi-scale Structural Similarity Index (Ms-ssim)mentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.

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Section: Multi-scale Structural Similarity Index (Ms-ssim)mentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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show abstract

“…Image compression aims to represent and transmit large original images using the fewest bytes possible and restore images with acceptable results. Deep Learning-based image compression [20] is one of the image compression techniques currently undergoing development.…”

Section: Fig 2 Dna Process Data Storagementioning

confidence: 99%

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

Muttaqin,

Herdiyeni,

Buono

et al. 2023

Int. J. Adv. Intell. Informatics

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The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction.

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“…Subsequently, the review will pivot towards unsupervised machine learning algorithms, unraveling their nuances and applications in the context of image denoising. This includes an in-depth analysis of methodologies such as clustering [13] and deep learning [14] [15]. Each algorithmic approach will be dissected to comprehend its architectural complexity, adaptability across different image domains, and efficacy in suppressing various noise types commonly encountered in practical imaging scenarios [16].…”

Section: A Introductionmentioning

confidence: 99%

Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review

Ferzo,

Abdulazeez

2024

ijcs

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The continuous evolution of imaging technologies has accentuated the demand for robust and efficient image denoising techniques. Unsupervised machine learning algorithms have emerged as promising tools for addressing this challenge. This review scrutinizes the efficacy, versatility, and limitations of various unsupervised machine learning approaches in the area of image denoising. The paper commences with a clarification of the foundational concepts of image denoising and the pivotal role unsupervised machine learning plays in enhancing its efficacy. Traditional denoising methods, encompassing filters and transforms, are briefly outlined, highlighting their insufficiencies in handling complicated noise patterns prevalent in modern imaging systems. Subsequently, the review delves into an exploration of unsupervised machine learning techniques tailored for image denoising. This includes an in-depth analysis of methodologies such as clustering deep learning. Each technique is surveyed for its architectural variation, adaptability, and performance in denoising diverse image datasets. Additionally, the review encompasses an evaluation of prevalent metrics used for quantifying denoising performance, discussing their relevance and applicability across varying noise types and image characteristics. Furthermore, it delineates the challenges faced by unsupervised techniques in this domain and charts prospective avenues for future research, emphasizing the fusion of unsupervised methods with other learning paradigms for heightened denoising efficacy. This review merges empirical insights, critical analysis, and future perspectives, serving as a roadmap for researchers and practitioners navigating the landscape of image denoising through unsupervised machine learning methodologies.

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Image Compression Based on Deep Learning: A Review

Cited by 17 publications

References 62 publications

Survey On Parameters of Data Compression

Survey On Parameters of Data Compression

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review

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